No relationship between fornix and cingulum degradation and within-network decreases in functional connectivity in prodromal Alzheimer's disease.

INTRODUCTION: The earliest changes in the brain due to Alzheimer's disease are associated with the neural networks related to memory function. We investigated changes in functional and structural connectivity among regions that support memory function in prodromal Alzheimer's disease, i.e., during the mild cognitive impairment (MCI) stage.
METHODS: Twenty-three older healthy controls and 25 adults with MCI underwent multimodal MRI scanning. Limbic white matter tracts-the fornix, parahippocampal cingulum, retrosplenial cingulum, subgenual cingulum and uncinate fasciculus-were reconstructed in ExploreDTI using constrained spherical deconvolution-based tractography. Using a network-of-interest approach, resting-state functional connectivity time-series correlations among sub-parcellations of the default mode and limbic networks, the hippocampus and the thalamus were calculated in Conn. ANALYSIS: Controlling for age, education, and gender between group linear regressions of five diffusion-weighted measures and of resting state connectivity measures were performed per hemisphere. FDR-corrections were performed within each class of measures. Correlations of within-network Fisher Z-transformed correlation coefficients and the mean diffusivity per tract were performed. Whole-brain graph theory measures of cluster coefficient and average path length were inspecting using the resting state data. RESULTS &
CONCLUSION: MCI-related changes in white matter structure were found in the fornix, left parahippocampal cingulum, left retrosplenial cingulum and left subgenual cingulum. Functional connectivity decreases were observed in the MCI group within the DMN-a sub-network, between the hippocampus and sub-areas -a and -c of the DMN, between DMN-c and DMN-a, and, in the right hemisphere only between DMN-c and both the thalamus and limbic-a. No relationships between white matter tract 'integrity' (mean diffusivity) and within sub-network functional connectivity were found. Graph theory revealed that changes in the MCI group was mostly restricted to diminished between-neighbour connections of the hippocampi and of nodes within DMN-a and DMN-b.

Introduction

Amnestic mild cognitive impairment (MCI) features a reduced ability to recall episodic events and to form new memories alongside an intact ability to function independently. MCI is a high risk factor for conversion to dementia, in particular Alzheimer’s disease (AD), with annual conversion rates of c10-20% [1,2]. AD is thought to evolve slowly and asymptomatically over decades, and current investigations of its earliest stages are mostly focused on MCI. This study aims to probe the relationship between structural and functional brain changes in MCI through use of resting state magnetic resonance imaging (rsMRI) and diffusion weighted imaging (DWI).

The use of in vivo imaging has established that whole-brain cerebral atrophy continues alongside the progression of AD and that these large-scale effects are reflected in the degeneration of a wide-range of behaviours [3]. Initial volume loss is understood to occur in the medial temporal lobe, specifically the hippocampus and entorhinal cortex [4,5]. This profile of cortical atrophy sometimes present in MCI is distinct from normal ageing. However, given the heterogeneity of MCI, it may not be sufficiently specific for a diagnosis of AD [6,7].

AD is widely described as a disconnection syndrome, i.e., it is the disconnection between brain areas that amplifies the cognitive and behavioural decline [8]. For example, it is proposed that early hypometabolism seen in the posterior cingulate cortex reflects distant damage in the hippocampal formation more so than local neuropathological processes within the posterior cingulate cortex [9,10]. Consistent with the disconnection hypothesis, correlations have been observed in AD between atrophy of white matter tracts related to episodic memory and grey matter atrophy of the hippocampal formation [11].

Synaptic loss and an accumulation of neurofibrillary tangles that disrupt cellular function are possible sources of disconnection [12,13]. Other candidate sources are grey matter atrophy that leads to Wallerian degeneration of white matter, and abnormalities that begin within white matter [14-16]. This last possibility has been investigated used DWI. This technique facilitates the examination of the diffusion of water molecules and can reveal between-group differences in the microstructure of white matter tracts. The causes of such changes can include myelination, axon density, axon diameter, membrane permeability and voxel architecture. It is not, however, possible to specify the exact change and best practice is to provide a range of anisotropy measures [17].

Lancaster et al., [18] found that DWI measures of the hippocampal cingulum and uncinate fasciculus, but not grey matter or white matter of the medial temporal lobe nor DWI measures of the fornix, predicted a three-year decline in episodic memory in cognitive healthy older people with AD risk factors. These findings add to other reports that white matter damage precedes grey matter atrophy [19-22]. Of note, the white matter tracts most implicated in MCI (the fornix, the cingulum bundle and the uncinate fasciculus) are those that facilitate communication and information transfer to and within medial temporal structures [20,23-28]. That is, white matter damage has been consistently identified in tracts related to regions where initial grey matter volume loss occurs. Nonetheless, it remains an open question as to whether grey or white matter atrophy occurs first or if both degenerate from the outset.

rsMRI assesses the brain’s intrinsic functional organisation through measurement of the blood-oxygen-level dependent signal when participants are at rest / not performing any task [29]. Functional connectivity refers to a synchrony in that signal between anatomically distinct regions (measured at rest or during task) that leads to the assumption that those regions are functionally connected [30]. rsMRI has revealed neural networks based on their functional connectivity [31], of these the default mode network (DMN) has been revealed to be widely implicated in MCI (for meta-analysis studies see: [32-34]) and to a lesser extent the limbic network [34]. In MCI functional connectivity between regions within the DMN has been observed as decreased and as enhanced [35,36], while within limbic network has been reported as enhanced [34]. Altered within and between-network functional connectivity has been implicated in other networks (e.g., somatomotor, executive control, dorsal attention) particularly as the disease progresses [37-40]. While functional connectivity enhancements are suggestive of a compensatory mechanism this is not necessarily the case [36]. Increased functional connectivity between the DMN and the frontoparietal network, for example, has been interpreted as a reflection of a difficulty in switching between optimal network behaviours [40].

That finding of increased functional connectivity parallels the lack of segregation between the DMN and frontoparietal networks that have been revealed using a graph theory approach in AD, and to a lesser extent, MCI patients [41]. Graph theory investigations of the brain look at the shape of information transfer at a high level, i.e., the network/connectome level. This focuses on examining how information is segregated within clusters thus facilitating specialisation (functional regions) and how it is integrated across clusters facilitating cross-modal collaboration [42,43]. Published graph theory studies support the idea of AD as a disconnection syndrome given that alterations in both information integration and segregation have been found across different neuroimaging modalities (e.g., EEG: [44,45]; structural: [46-48]; DWI: [49]; rsfMRI: [50,51]).

Previous studies have observed changes in the relationship between structural and functional connectivity in MCI centring on the thalamus [52,53]. This study adds to the literature by investigating constrained spherical deconvolution DWI measures [54] of MCI-targeted white matter tracts (fornix, cingulum bundles, and uncinate fasciculus) and temporal correlation connectivity measures of MCI-targeted functional networks (DMN and limbic), and by examining the relationship between those structural and functional measures. A correlation between these white matter tracts and functional networks would suggest a (non-directional) dependence between their degeneration in MCI. Further, we investigated functional connectivity graph theory measures to provide an alternative and higher level perspective on any MCI related changes.

Methods

Twenty-eight older adults with amnestic MCI participated in the study. One person was unable to undertake scanning due to undiagnosed claustrophobia, upon data inspection a second person was excluded due to discovery of an undiagnosed historical focal thalamic lesion. A third person was eliminated due to missing demographic and neuropsychological information. Five MCI people declined to complete the entirety of the neuropsychological testing set (four did not complete the CERAD tests, one did not complete the CERAD or GDS tests)–however, all were successfully scanned. The final sample included 25 MCI participants.

Twenty-three old healthy controls (HC) were recruited from the greater Dublin area via newspaper advertisements. The MCI participants were recruited from Dublin memory clinics in Tallaght University Hospital, St James’ Hospital and St Patrick’s Hospital. All participants were right-handed and 54–80 years old. Exclusion criteria covered a history of stroke, transient ischaemic attack, heart attack, head injury, neurological illness, psychiatric illness, substance addiction or abuse, abnormal hearing or vision (in presence of necessary correction). The MCI participants were diagnosed by a clinician according to the Petersen criteria [55], i.e., absence of functional decline indicative of dementia but presence of abnormal memory scores relative to age and educational attainment.

The study was conducted in line with the Declaration of Helsinki principles, and it received ethical approval from the St Patrick’s University Hospital and Tallaght University Hospital Research Ethics Committees. All participants gave written consent prior to taking part in the study.

Neuropsychological Testing

All participants undertook a health screening questionnaire to assess suitability for scanning. The Consortium to Establish a Registry for Alzheimer’s Disease assessment (CERAD, [56]) was used to screen the HCs for undiagnosed age-related cognitive impairment [57]. Further, participants were tested with the mini-mental state examination (MMSE; [58], short-form Geriatric Depression Scale (GDS; [59]), and a Cognitive Reserve Questionnaire (CogR; [60]) before the MRI scan.

Robust independent t-tests were performed on the demographic and neuropsychological tests using the Yuen t-test [61] (bootstrapped and 10% trimmed) from the R package WRS2 [62]. The t-tests revealed that the groups did not differ in age, gender, number of years of education, cognitive reserve or IQ (all Ps > .05). It was observed that the MCI group performed less well overall on the CERAD battery than the healthy controls. Bonferroni corrected one-sided t-tests revealed statistically worse scores for MMSE, word delay, word recognition (yes), and trail B. For summary details see Tables 1 and 2. Using the CERAD scores standardised against age, education and gender norms, a statistically lower performance was observed for MMSE, word delay, trial A and trial B–see Table A in S1 File. The MCI group scored higher on the depression scale (p = .03). Three people in the MCI group and one in the HC group exceed the short-form GDS cut-off score of 5 suggesting depression. Depression is thought to accompany but not precede the development of MCI [63] and to be an additional risk factor in conversion from MCI to AD [64]. MCI depression score did not correlate with those cognitive measures that survived correction for multiple comparisons. There were, however, correlations with the standardised measures of fluency (p = .002) and naming (p = .032) that unexpectedly indicated increasing cognitive scores with increasing GDS score–see Figs A and B in S1 File for further details.

Table 1

Demographic details.

Profile	HC	MCI	Statistic	P-value*
Gender (M/F %)	65/35	44/56	X2 = 2.17	0.141
Age	69 ± 2.66	68 ± 6.28	Yt = -0.7369	0.441
Education	14 ± 3.85	12.6 ± 2.75	Yt = -1.2592	0.1995
IQ	118 ± 7.87	114 ± 6.72	Yt = -1.6943	0.107

Raw mean + SD

* p-value: 2-sided, uncorrected

Table 2

Neuropsychological tests.

Measure	HC	MCI	Statistic (Yt)	P-value*
GDS	1.04 ± 1.72	2.25 ± 2.07	2.53	0.030
Cognitive Reserve	17.2 ± 4.99	15.68 ± 4.86	-0.9464	0.347
CERAD:
MMSE	28.9 ± 0.949	26.7 ± 2.34	-3.5309	0.001
Fluency	17.7 ± 3.55	15.3 ± 4.87	-1.9598	0.056
Naming	14.7 ± 0.559	12.9 ± 2.25	-2.9748	0.016
Word Delay	8.6 ± 1.27	4.21 ± 2.39	-6.5326	0.000
Word Recognition Y	9.9 ± 0.209	8.65 ± 1.53	-3.6551	0.0095
Word Recognition N	10 ± 0	9.60 ± 0.821	-1.51	0.134
Praxis	10.5 ± 0.846	10.1 ± 0.898	-1.5223	0.100
Trail A	35.9 ± 7.68	48.9 ± 17.7	2.4595	0.025
Trail B	74.5 ± 21.0	139 ± 58.4	4.9546	0.0005

Raw score mean + SD

* p-value: 2-sided, uncorrected.

MRI Data acquisition

All data was acquired on a 3.0 Tesla Philips Achieva MR system (Best, The Netherlands) with an eight channel head coil. A high-resolution 3D T1-weighted anatomical image was acquired: Echo time (TE) = 3.9 ms, repetition time (TR) = 8.4 mm, field of view (FOV) = 230 mm, slice thickness = 0.9 mm, voxel size = 0.9 m x 0.9 mm x 0.9 mm, total scan time was 5 min 46 s. For the DWI acquisition whole-brain high angular resolution diffusion imaging (HARDI) data was acquired using a parallel sensitivity encoding (SENSE) approach [65] with a reduction factor of two. It was acquired using single-shot spin echo-planar imaging (EPI): TE = 81 ms, TR = 14,556 ms, FOV = 224 mm, matrix 112 x 112, voxel size = 2 mm x 2 mm x 2 mm, and 65 slices with 2 mm thickness and no gaps, total scan duration was 18 min 6 s. Diffusion gradients were applied in 61 isotropically distributed orientations with b = 2000 s/mm2, four images with b = 0 s/mm2 were also acquired.

Data acquisition for the rsMRI lasted 7 min. A T2*-weighted echo-planar imaging sequence with TE = 27 ms and TR = 2000 ms was used to acquire the blood oxygenation dependent (BOLD) signal. Two hundred and ten volumes of data were acquired, voxel size = 3.5 mm x 3.5 mm x 3.85 mm with a 0.35 mm gap between slices. Thirty-nine slices, covering the entire brain, were imaged per volume. Slices were acquired in an interleaved sequence from an inferior to superior direction. During this scan, participants were instructed to fixate on a red cross-hair in the centre of a screen behind the MRI scanner, it was visible via a mirror.

Pre-processing and data extraction

T1-w: T1-w images were oriented to standard position (FSL; [66]), labelling was verified and images were visually assessed for quality and incidental findings. FSL-ANAT segmentation method was used to extract tissue volumes (grey matter, white matter and cerebrospinal fluid) in order to estimate intra-cranial volume.

DWI: The Philips diffusion-weighted images were converted to Nifti files using dcm2niix [67]. They were then pre-processed using ExploreDWI version 4.8.4 [68]. This included converting the Philips bval and bvec files to a bmatrix (txt) file. B0 fieldmaps of the bmatrix and Nifti files were brought to the beginning of the images as appropriate. The Nifti files were made ExploreDWI compatible, gradients were permuted and flipped as required and the files converted to matlab image files. Corrections for subject motion, eddy current and EPI were made in one step using the Robust approach (Rekindle linear), during which the images were registered to their respective ExploreDWI-compatible T1-w files, using the methods described elsewhere [69,70].

Whole-brain tractography was run on the corrected files using a constrained spherical deconvolution method [71], a deterministic approach. This method can account for complex white matter orientation such as crossing fibres [72] and has previously detected changes in MCI and AD [54]. Using every voxel as a seed point, and in increments of 1 mm, the principal diffusion orientation at each point was estimated. Tracking moved along the direction that subtended the smallest angle to the current trajectory. A trajectory was followed until the scaled height of the fibre orientation density function peak dropped below 0.1, or the direction of the pathway changed through an angle of no more than 30°.

Following whole-brain tractography, the different tracts were extracted by manually drawing several regions of interest (ROIs) defined according to published methods for the fornix [73,74], the subgenual and retrosplenial branches of the cingulum [75], the parahippocampal branch of the cingulum [76], and the uncinate fasciculus [73]. See Fig 1 for further details on the placement of the ROIs.

Fig 1

Region of interest placement for each white matter tract.

Within ExploreDTI the blue lines indicate a ‘seed/or’ gate, the green line an ‘and’ gate, red lines indicate ‘not’ gates. Tracts are shown for the left hemisphere in a template subject. A) fornix, B) parahippocampal cingulum, C) retrosplenial cingulum, D) subgenual cingulum, and E) uncinate fasciculus.

Using an atlas-based tractography approach the ROIs were located on three template individuals and then applied to participants pre-matched to each template-individual. Each template individual was chosen to represent those with small, medium or large ventricles, identified as such based on visual inspection, and classification, of the entire group of participants. The atlas-based tractography approach spatially transforms the ROIs, manually drawn on the templates, to the other subjects’ native space. This ensures consistency in the identification of the tracts. The use of three templates did not always overcome inter-subject variability issues, as was evident in missing or slight tracts or tracts with excessive spurious streamlines. In such cases (17% for fornix, 8% for cingulum bundles, 44% for uncinate fasciculus) the ROIs were manually drawn and adjustments were made as necessary. In all cases tidying of the tracts was achieved by the application of one or several NOT gates.

Resting State fMRI: The rsMRI data were processed using the Conn v18a toolbox [77] run in SPM v8 [78]. A default MNI152-space data pre-processing template was applied consisting of: functional realignment and unwarping, slice-timing correction, structural segmentation and normalisation, functional normalisation, outlier detection, and smoothing. Segmentation and normalisation steps were supported by the acquired structural T1-w images. Structural target resolution was set at 1 mm isotropic, functional target resolution was set at 2 mm isotropic. Smoothing was done using a 4 mm full-width-at-half-maximum Gaussian kernel.

Using Conn default settings, potential confounding effects removed from the BOLD signal using linear regression were: white matter and cerebrospinal fluid time series (5 regressors each, CompCor approach, [77], scrubbing (invalid scans: M = 6.71, SD = 12.3, range 0–54, no difference between groups, Yt = 1.09, p = .309 ), realignment (6 motion parameters and 6 first-order temporal derivatives) and the effect of rest. Band-pass filtering (0.01–0.08 Hz) and linear detrending were included in this denoising step.

The BOLD signal time series was extracted from sub-cortical regions and cortical networks known to be implicated in early Alzheimer’s Disease [79]. The hippocampus and thalamus were identified using the FSL Harvard-Oxford Atlas (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases). Using the 2mm 400 region cortical atlas [80] two limbic (a and b) and three default mode sub-networks (a, b, and c) per hemisphere were identified. These atlas parcellations were computed from functional connectivity patterns. The sources of the functional connectivity signals within each sub-network are detailed in Fig 2. Centroid co-ordinates for the parcels are presented in S1 File Table L. All atlases and the pre-processed resting state images were in MNI152 space. A weighted sum time series method was used to extract the BOLD time series signals for each ROI. Connectivity measures were calculated using a haemodynamic response function weighted general linear model for bivariate correlations, set at a default 0.25 threshold. Using the Conn toolbox this step outputs Fisher Z-transformed correlation coefficients per ROI-to-ROI pairing for each participant.

Fig 2

The three parcellations of the DMN and two parcellations of the limbic networks as delineated by the Schaefer et al., 2018 atlas [80].

DMN-a: 1–2) inferior parietal lobule 1&2, 3–5) dorsal prefrontal cortex 1–3, 6–12) posterior cingulate cortex 1–7, 13–18) medial prefrontal cortex 1–6. DMN-b: 1–6) temporal cortex 1–6, 7–8) inferior parietal lobule 1&2, 9–14) dorsal prefrontal cortex 1–6, 15–16) left prefrontal cortex 1&2, 17–21) ventral prefrontal cortex 1–5. DMN-C: 1) inferior parietal lobule 1, 2–4) retrosplenial cortex 1–3, 5–7) parahippocampal cortex 1–3. Limbic a: 1–5) orbitofrontal cortex 1–5. Limbic-b: 1–7) temporal pole 1–7.

To estimate global measures of graph theory the above steps for functional connectivity correlations were also followed, but this time the signal was extracted from the whole brain. All sub-cortical and cerebellar segmentations were defined using the FSL Harvard-Oxford Atlas (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases), and all cortical ROIs were extracted from the Schaefer et al., [80] 400 ROI atlas. Measures of cluster coefficient and average path length were inspected at a cost (sparsity) level of 0.15.

Analyses

Due to the general concern regarding small sample sizes meeting the assumptions of the general linear model, a robust approach was undertaken [81]. The R package robustbase [82] was used to perform robust multiple linear regressions. Its lmrob function fits a model based on an M-estimator using iteratively reweighted least squares estimation [83]. Linear regressions of diffusion-weighted measures and resting-state connectivity measures conditional upon group were performed per hemisphere. All tests controlled for age, education and gender. The relationship between depression and cognitive function is complicated and some depressive elderly will not convert to dementia [84]. However, we chose not to include depression as a control covariate given that its presence may reflect dementia pathology [85]. That is, controlling for depression would run the risk of removing relevant explanatory variance. Further, in this group it would both reduce sample size as one participant declined to complete the GDS and risk over-fitting the model. In any case, in this cohort depression did not correlate with worsening measures of cognition (see Fig B in S1 File). FDR-corrections were performed within each class of measures. With the exception of the graph theory analysis, the relevant measures were extracted from their respective processing package and analysed within R version 3.5.0 [86]. Additional R packages used were dpylr [87], ggplot2 [88], stringr [89].

The diffusion metrics extracted for analysis were fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (Da) and radial diffusivity (Dr) and tract volume. Lower values of FA, and higher values of MD, Da and Dr, were predicted in the MCI compared to the HC group [90]. Tract volume was divided by total intracranial volume in native space prior to statistical testing (metric = mm3). Tract volume was predicted to be lower in the MCI group. P-values were adjusted to take into account the directional hypotheses.

Fisher Z-transformed bivariate correlation coefficients of BOLD signal time series were averaged/calculated within the chosen networks, between the networks, and between the networks and the subcortical ROIs, per hemisphere. No directional predictions were made given that both decreased and increased connectivity have previously been observed within AD samples [46]. (Within hemisphere only analyses were conducted, across hemisphere connectivity was not inspected due to sample size).

Within group and per hemisphere, robust percentage bend correlations of the within-network Fisher Z-transformed bivariate correlation coefficients and the normalised MD values of each tract were performed using the using the WRS2 package [62]. These inspected associations were not limited to known structural connectivity (e.g. fornix and DMN-c) based on two assumptions. First, human structural connectivity is not yet so precisely delineated that exclusive connections are assured (e.g., the uncinate fasciculus likely facilitates connections with various networks such as limbic-a, limbic-b networks, DMN-a, and DMN-c). Second, taking an agnostic approach allowed possible secondary, or downstream, effects of tract degeneration to be considered [91,92]. However, in order to constrain the number of analyses, these correlations were focused on the MD metric, as it is understood to be the diffusion measure most sensitive to AD changes [24,93]. Associations of tract MD with between-network functional connectivity (over 200 possible correlations) were not inspected in order to constrain the analysis.

Between-group rsMRI graph theory measures of cluster coefficient and average path length were inspected in order to provide a global overview of functional connectivity brain changes. Cluster coefficient is a local measure that examines the number of nearest neighbours of a node as a proportion of the maximum possible number of connections. From the connectome perspective it measures segregation—the efficiency of information transfer at a local scale. Path length is a global measure of integration; it quantifies the overall routing efficiency of a network by examining the average minimum number of connections that link any two nodes of a network [42]. These analyses were conducted within the Conn toolbox. Cost (sparsity) was set at 0.15, no directional prediction was made. The tests controlled for age, education and gender and FDR-corrections were applied to follow-up tests.

Results

DWI

All diffusion measures of the fornix showed evidence of degeneration in the MCI group: MD, Da and Dr were comparatively increased in the left (MD: t = 4.21, p = .0006, Da: t = 5.32, p = .00005, Dr: t = 3.74, p = .002) and the right fornix (MD: t = 5.06, p < .0001, Da: t = 5.59, p = .00005, Dr: t = 4.65, p = .0002), and FA was comparatively decreased in the left (t = -2.78, p = .019) and right (t = -2.42, p = .035) fornices. In the MCI compared to the HC group, MD and Da were significantly increased in the left parahippocampal cingulum (MD: t = 2.72, p = .019, Da: t = 3.02, p = .012), and MD and Dr were significantly increased in the left retrosplenial cingulum (MD: t = 3.08, p = .011, Dr: t = 2.43, p = .035) and in the left subgenual cingulum (MD: t = 3.08, p = .011, Dr: t = 2.77, p = .019).

All other measures were in the expected direction (i.e., comparatively decreased FA and tract volume and increased MD, RD and AD in the MCI group) but either did not survive corrections for multiple comparisons or were not significant at the uncorrected level. There were two exceptions to this, Da values were the same for each group in the left uncinate fasciculus and were comparatively decreased in the right uncinate fasciculus in the MCI group. Details of the MD values are presented in Table 3, Figs 3 and 4 display the DWI metrics for the fornix and parahippocamapl cingulum, see S1 File for other diffusion measure descriptives (Tables B-F) and graphs (Figs C-E).

Table 3

MD values per structure per hemisphere.

Structure	Hemi	HC	MCI	Estimate	t-statistic	p-value *
Fornix	LH	0.00120 ± 0.00008	0.00132 ± 0.00014	0.000139	4.210	0.000064
	RH	0.00118 ± 0.00008	0.00127 ± 0.00008	0.000100	5.056	.0000042
Para-hippocampal cingulum	LH	0.00072 ± 0.00003	0.00075 ± 0.00005	0.0000187	2.721	0.00467
	RH	0.00072 ± 0.00003	0.00074 ± 0.00005	0.0000104	1.007	0.15950
Retrosplenial cingulum	LH	0.00067 ± 0.00002	0.00069 ± 0.00002	0.0000213	3.084	0.00178
	RH	0.00067 ± 0.00002	0.00068 ± 0.00003	0.0000148	2.052	0.02315
Subgenual cingulum	LH	0.00069 ± 0.00002	0.00070 ± 0.00002	0.00001516	3.078	0.00181
	RH	0.00069 ± 0.00002	0.00069 ± 0.00002	0.00000946	1.706	0.04765
Uncinate fasciculus	LH	0.00070 ± 0.00002	0.00071 ± 0.00004	0.00000444	0.806	0.21225
	RH	0.00072 ± 0.00002	0.00072 ± 0.00003	0.00000254	0.336	0.36900

Hemi = hemisphere, LH = left hemisphere, RH = right hemisphere

*p-values are uncorrected, 1-sided

Fig 3

Mean DWI measures of the fornix.

Fig 4

Mean DWI measures of the parahippocampal cingulum.

rsMRI

In MCI compared to HC, statistically smaller within-network connectivity was found in the DMN-a in the left (t = -3.38, p = .010) and right (t = -3.75, p = .005) hemispheres. See Table 4.

Table 4

Within network functional connectivity.

Measure	HC	MCI	Estimate	t-statistic	P-value*
Within LH:
DMN-a	0.506 ± 0.078	0.416 ± 0.133	-0.100	-3.379	0.00156
DMN-b	0.352 ± 0.105	0.291 ± 0.062	-0.058	-2.552	0.01435
DMN-c	0.511 ± 0.129	0.421 ± 0.118	-0.085	-2.535	0.0150
Limbic-a	0.413 ± 0.126	0.404 ± 0.178	-0.002	-0.044	0.965
Limbic-b	0.251 ± 0.098	0.265 ± 0.079	-0.009	-0.315	0.754
Within RH:
DMN-a	0.485 ± 0.109	0.381 ± 0.122	-0.119	-3.751	0.000522
DMN-b	0.302 ± 0.105	0.275 ± 0.096	-0.033	-1.194	0.239
DMN-c**	0.462 ± 0.132	0.368 ± 0.108	-0.093	-2.568	0.0135
Limbic-a	0.349 ± 0.142	0.353 ± 0.122	0.006	0.147	0.884
Limbic-b	0.225 ± 0.074	0.257 ± 0.088	0.018	0.679	0.501

* p-values are uncorrected 2-sided

** No covariates were included due to non-convergence of model

In MCI compared to HC, statistically smaller between-network connectivity was found between DMN-a and DMN-c, between DMN-a and the hippocampus, and between DMN-c and the hippocampus in both the left hemisphere (respectively: t = -4.63, p = .002; t = -4.21, p = .003; t = -3.99, p = .003) and the right hemisphere (respectively: t = -3.81, p = .005; t = -3.31, p = .011; t = -3.52, p = .008). In the right hemisphere only in MCI compared to HC, statistically smaller between-network connectivity was found between DMN-c and the thalamus (t = -3.02, p = .021) and between DMN-c and the limbic-a network (t = -4.01, p = .003). See Table 5, and Figs 5 and 6, for results mentioned here and Table G in S1 File for the complete set.

Table 5

Highlights of between network functional connectivity.

Measure	HC	MCI	Estimate	t-statistic	P-value*
LH
DMN -a & -c	0.352 ± 0.075	0.251 ± 0.111	-0.108	-4.63	0.0000336
DMN-a & Hippocampus	0.238 ± 0.117	0.114 ± 0.125	-0.149	-4.21	0.000127
DMN-c & Hippocampus	0.409 ± 0.096	0.262 ± 0.138	-0.157	-3.99	0.000254
RH
DMN -a & -c	0.328 ± 0.089	0.217 ± 0.092	-0.116	-3.81	0.000438
DMN-a & Hippocampus	0.208 ± 0.123	0.104 ± 0.156	-0.128	-3.31	0.00192
DMN-c & Hippocampus	0.326 ± 0.124	0.186 ± 0.137	-0.157	-3.52	0.00102
DMN-c & Thalamus**	0.147 ± 0.091	0.039 ± 0.144	-0.109	-3.02	0.00408
DMN-c & Limbic-a	0.133 ± 0.085	0.080 ± 0.069	-0.082	-4.01	0.00024

*p-values are uncorrected 2-sided

** No covariates were included due to non-convergence of model

Fig 5

The red lines with beta values indicate where between region functional connectivity was higher in HC compared to MCI in the left hemisphere.

Fig 6

The red lines with beta values indicate where between-region functional connectivity was higher in HC compared to MCI in the right hemisphere.

DWI and rsMRI

Robust correlations between the normalised MD of each of the five tracts and each of the five within-network Fisher Z-transformed correlations were conducted per hemisphere and group (total of 5x5x2 correlations). At the uncorrected level two associations were significant–between the right parahippocampal cingulum and Limbic-a in the MCI group (r = -0.525, p = .007), and between the left retrosplenial cingulum and Limbic-a in the healthy controls (r = -0.524, p = .010)–but neither survived corrections for multiple comparisons. Given these results, no between-group comparisons were carried out. See Figs F-J in S1 File for full details.

Graph theory

Graph theory analyses, at sparsity level of 0.15, revealed a significant between-group difference in the cluster coefficient measure. This was higher in the HC (M = 0.492, SD = 0.027) compared to the MCI (M = 0.474, SD = 0.033) group (b = .02, t = -2.53, puncor = .015). This difference was driven by 18 ROIs—8 in DMN-a (5 RH, 3 LH), 5 in DMN-b (2 RH, 3LH), both hippocampi, two ROIs in the LH somatomotor network and one in the RH salient ventral attention network–all of which survived FDR-correction. See Fig 7 and Table 6 for details. The difference in average path length between the two groups did not reach statistical significance (b = .03, t = -1.94, puncor = .059; HC: M = 2.08, SD = 0.049; MCI: M = 2.06, SD = 0.06).

Fig 7

Points represent regions where the cluster coefficient was significantly larger in HC compared to MCI, at sparsity level of 0.15.

Both hemispheres are presented on the same view. 1) RH DMN-a PFCm_1; 2) RH DMN-a PFCd_2; 3) LH DMN-a PCC_2; 4) LH DMN-b Temp 3; 5) LH DMN-b PFCd_5; 6) RH hippocampus; 7) LH DMN-a PFCm_1; 8) LH DMN-a PFCd_3; 9) RH DMN-a PFCm_4; 10) RH DMN-b AntTemp_1; 11) LH DMN-b PFCd_1; 12) RH DMN-b PFCd_4; 13) RH DMN-a PCC_1; 14) RH DMN-a PFCm_3; 15) LH SomMot_a 16; 16) LH hippocampus; 17) RH SalVentAttn_a ParMed_7; 18) LH SomMot_a 12. PFC = prefrontal cortex; d = dorsal; m = medial; Temp = temporal; Ant = anterior; SomMot = somatomotor; SalVentAttn = salient ventral attentional.

Table 6

Graph theory cluster coefficient, sparsity level of 0.15.

Measure	HC	MCI	Estimate	t-statistic	P-value*
LH
DMN-a PCC 2	0.569 ± 0.045	0.510 ± 0.067	0.07	-4.17	0.000146
DMN-b Temp 3	0.564 ± 0.054	0.481 ± 0.096	0.09	-3.98	0.000262
DMN-b PFCd 5	0.540 ± 0.087	0.454 ± 0.089	0.10	-3.79	0.000458
DMN-a PFCm 1	0.536 ± 0.065	0.471 ± 0.066	0.07	-3.65	0.000713
DMN-a PFCd 3	0.525 ± 0.073	0.469 ± 0.071	0.07	-3.53	0.001002
DMN-b PFCd 1	0.543 ± 0.065	0.471 ± 0.075	0.08	-3.49	0.001117
SomMoTA 16	0.546 ± 0.089	0.458 ± 0.104	0.10	-3.42	0.001383
Hippocampus	0.501 ± 0.094	0.429 ± 0.081	0.09	-3.35	0.001687
SomMoTA 12	0.544 ± 0.089	0.466 ± 0.089	0.09	-3.29	0.002004
RH
DMN-a PFCm 1	0.532 ± 0.062	0.451 ± 0.071	0.09	-4.77	0.000022
DMN-a PFCd 2	0.559 ± 0.061	0.466 ± 0.080	0.10	-4.46	0.000058
Hippocampus	0.514 ± 0.123	0.421 ± 0.093	0.11	-3.78	0.000472
DMN-a PFCm 4	0.542 ± 0.062	0.477 ± 0.071	0.07	-3.52	0.001023
DMN-b Ant Temp 1	0.541 ± 0.063	0.467 ± 0.096	0.09	-3.51	0.001052
DMN-b PFCd 4	0.558 ± 0.066	0.488 ± 0.101	0.09	-3.46	0.001215
DMN-a PCC 1	0.573 ± 0.057	0.512 ± 0.069	0.07	-3.46	0.001222
DMN-a PFCm 3	0.507 ± 0.086	0.452 ± 0.075	0.08	-3.43	0.001335
SalVentAttn A ParMed7	0.497 ± 0.067	0.437 ± 0.057	0.06	-3.34	0.001735

* p-values are uncorrected 2-sided

Given the challenges of defining a-priori an appropriate network cost level [94], and following feedback, we additionally inspected the graph theory measures at sparsity levels of .10, .20 and .25. There were significant differences between the two groups in the cluster coefficient measure for sparsity levels of 0.10, 0.20 and 0.25 (uncorrected for multiple comparisons across levels). As per cost level 0.15, the cluster coefficient measures were higher in the HC compared to the MCI group. At level 0.10 the difference was driven by 16 ROIs (12 from the DMN and hippocampus– 10 of which drive the group differences at sparsity level 0.15). At level 0.20 no individual ROI survived correction for multiple comparisons. At level 0.25 the difference was driven exclusively by LH temporal area 3 of the DMN_b (it was also a driver of differences at levels 0.10 and 0.15). Full details are provided in S1 File. There was a significant difference in the average path length between the groups at sparsity level of 0.10. As per sparsity level 0.15, this average path length difference was not present at levels 0.20 and 0.25. Further details are provided in S1 File.

Discussion

This study employed multimodal imaging to investigate relationships between brain regions known to be impaired early in AD. The microstructures of five relevant white matter tracts were analysed using DWI measures. rsMRI was used to investigate functional connectivity across implicated parcellated networks and sub-cortical regions. Relationships between these respective measures of tract ‘health’ and connectivity ‘health’ were assessed. Finally, to provide a high-level overview, global measures of graph theory were extracted from rsMRI correlations of the entire brain.

MCI-related disturbances in white matter structure were found in the fornix, in the left parahippocampal cingulum, the left retrosplenial cingulum and the left subgenual cingulum. No such changes were found in the uncinate fasciculus. Functional connectivity decreases were observed in the MCI group within the DMN, but not the limbic, sub-networks. Functional connectivity was decreased in the MCI group, between the hippocampus and sub-areas a and c of the DMN, between DMN-c and DMN-a, and, in the right hemisphere only, between DMN-c and both the thalamus and limbic-a. No relationships between white matter tract ‘health’ (MD metric) and within sub-network functional connectivity were detected. The observed region-of-interest functional connectivity disturbances were broadly reflected in the whole-brain cluster coefficient measure of graph theory. It revealed that impact of the putative AD-related pathology in the MCI group was observed in, and mostly restricted to, between-neighbour connections of the hippocampi and of nodes within DMN-a and DMN-b.

White matter tractography studies of MCI and early stage AD have found that absolute measures of diffusivity (MD, Dr, Da) are more sensitive detectors of pathology compared to ratio measures such as FA, which reflect changes in the shape of the diffusion ellipsoid [24,25,93,95,96]. This pattern of diffusion metrics is reflected in the present results, with only absolute diffusivity measures reaching (uncorrected and corrected) statistical significance in the cingulum branches.

Damage in the left hippocampal cingulum is the most consistent finding across different types of DWI analysis and stage of MCI [24]. In the present study white matter changes in the cingulum reached corrected statistical significance in the left hemisphere only. Lateralised tract damage has been previously reported, e.g., increased MD in the right posterior cingulate fasciculus in MCI [97]; increased MD in the left cingulum bundle in MCI [98]; decreased FA in left parahippocampal cingulum in MCI [99], increased FA in the left anterior temporal lobe in AD [100], decreased Dr in left uncinate fasciculus in AD [101]. Nonetheless, bilaterally the overall white matter changes found in the current paper were in the expected direction [102,103]. This directional effect is true also of the uncinate fasciculus, although, unlike published studies [97,104] we did not find a statistically significant MCI-related change.

The rsMRI results reveal that the strength of within-sub-network functional connectivity is reduced in the DMN (in DMN-a only at corrected p-value) but not in the limbic networks in the MCI group. DMN-c (retrosplenial cortex, parahippocampal cortex and inferior parietal nodes) and the hippocampus were implicated in the observed decreases in between-network connection strength in the MCI group. This reflects the findings that the medial temporal lobe is the originating grey matter site of damage in AD [4,5]. No evidence of increased connectivity (putatively compensatory or reflective of switching difficulty) was found as has been reported elsewhere [40].

In the current sample global white and grey matter atrophy are present in the MCI group–(see Table K and Fig K in S1 File). However, while both the white matter and the connectivity strength analyses reveal insults to the system, no relationship between the different types of damage was apparent. White matter and grey matter damage in AD may or may not occur independently [19,105]. [106] observed white matter network alterations in preclinical AD that preceded cortical atrophy and hypoglucose metabolism. The retrogenesis hypothesis has been suggested as a putative mechanism for that order of attack [107,108]. However, white matter damage may also be secondary to grey matter damage via Wallerian degeneration [14-16]. The lack of relationship between structural insult and functional dysconnectivity seen here may be indicative of independent and non-interacting degenerative processes during MCI (in this sample) or it may be related to limitations of our chosen analysis.

Specifically, it may be that the functional parcellations used are too coarse (e.g., the DMN-a is composed of 18 functionally connected regions) to probe structural and functional relationships. It is also possible that combinations of damage to different tracts, rather than individual tract damage as examined here, are related to within-network dysconnectivity, especially at the early stages of degeneration [109-111]. We did not examine such multivariate relationships due to sample sizes constraints. In addition, it is possible that damage in non-examined white matter tracts, including superficial and short range association fibres, may influence functional connectivity within the DMN in MCI [112,113]. It is also possible that, notwithstanding the simple statistical approach we used, the sample size is simply too small (see limitations section). Separately, or in combination, these factors may have constrained our ability to detect a relationship between structural and functional damage in MCI.

The whole-brain graph theory measures revealed that the areas of difference between the two groups were centred on DMN nodes and the hippocampus. The analysis of the cluster coefficient (how well specialist information is segregated) showed that across both hemispheres there were fewer connections-between-nearest-neighbours of select DMN-a and DMN-b nodes and of the hippocampus in the MCI group, at cost levels of 0.10 and 0.15. This metric, related to the resilience of local networks, suggests that these areas in the MCI group are relatively more exposed to insult [114,115]. The presence of the hippocampus and DMN-a in the results from both analyses (functional connectivity strength and graph theory) may indicate that it has both lost connections and that its remaining connections are also weaker.

Limitations

The heterogeneity of the MCI sample is a limiting factor in the interpretation of each analysis approach. The most common cause of MCI is AD-rleated pathology, however, other causes such as Lewy body disease and vascular insults in isolation or in combination are also common [6,7]. Further, not all cases of MCI go on to express further decline [116]. This heterogeneity, likely present in the current sample, introduces variation in the data that may hide or accentuate AD-related degeneration. Future studies incorporating protein-based diagnostic criteria will eventually minimise this confound. The current sample size is small and the power to detect a medium effect size (Cohen’s d = 0.05) is approximately 52% in directionally predicted tests and 40% in two-sided tests (calculated using the pwr package for R [117]).

The rsMRI ROI approach is heavily dependent on the spatial accuracy of the boundaries of the chosen templates to reflect the functional organisation of the brain [118]. The difficulty in achieving perfect registration to such templates, particularly in the case of neurodegeneration, should be taken into consideration when interpreting results. Additionally, a region-of-interest approach, by definition, excludes brain regions from assessment and thus over-simplifies findings–in this case the differences between MCI and HC groups [34]. Also relating to the rsMRI data, bivariate correlations between regions were examined, this approach runs the risk of detecting spurious (or accentuating) connections between two areas if both those areas are connected to a common third area [118]. The group contrast of the current analysis may both help (by cancelling out common indirect connections) and hinder (by exposing spurious/indirect connections through contrast) this problem.

Constraints regarding the interpretation of graph theory analysis include those mentioned above for rsMRI (excluding the region of interest approach) and are extended by its binarisation process [119]. In order to achieve a high-level overview, it meant that in this instance, only a restricted range of cost % of connections based on correlation strength were included in the analysis. With this reductive approach valuable information is lost and the risk of a skewed understanding of clinically important brain connectivity differences is increased [118]. It should also be noted that a widespread difference in underlying functional connectivity between patient and control groups may introduce potential artifactual differences in network topology metrics [120].

Finally, a limitation of the DWI approach employed is the subjectivity introduced by the manual identification of ROIs and any cleaning of spurious tracts. To minimise this subjectivity we used published guides for the placement of tract delineators and the atlas-based approach whereby the ROI definition was applied across the entire group. We also facilitated natural variability by creating three atlases (small, medium, large) according to a subjective assessment of ventricle size. Despite these precautions we cannot eliminate this limitation, however, we can be confident that the results are directionally consistent with existing literature.

Conclusion

We found white matter damage to the fornix and sub-divisions of left cingulum bundle, reduced connectivity strength within DMN-a, and reduced connectivity between the hippocampus and DMN-c, the hippocampus and DMN-a, and reduced information segregation (cluster coefficient) within the DMN and hippocampus in a group of MCI participants. However, we found no relationship between white matter disturbance and functional connectivity strength. This may be a reflection of independent degeneration processes in white and grey matter, particularly during early stage AD. Alternatively, the lack of relationship between the functional and structural measures may be related to study design and analytical factors.

(DOCX)

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8 Jul 2019

PONE-D-19-16445

No relationship between fornix and cingulum degradation and within-network decreases in functional connectivity in preclinical Alzheimer’s disease

PLOS ONE

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Reviewer #1: This paper investigated changes in functional and structural connectivity among regions that support memory function in preclinical Alzheimer’s disease. Then investigated the relationship between white matter tracts (fornix, cingulum bundles, and uncinate fasciculus) and functional connectivity. It’s very interesting and meaningful. But there are some issues to be resolved as following.

Main concerns:

1. L313: “Cost (sparsity) was set at 0.15”. How about other costs? Are the results similar or seriously different?

2. L292: “Within hemisphere only analyses were conducted, across hemisphere connectivity was not inspected due to sample size”. It’s better and possible to make a comparison across hemisphere because authors didn’t use multivariate.

3. L272: “Its lmrob function fits a model based on an M-estimator using iteratively reweighted least squares estimation”. Could you list the model equation in details?

Minor concerns:

1. the figures aren’t with enough resolution, especially the title and number are unclear.

2. Table 3,4, why t(43), what does 43 mean? And the table format should be revised for consistency.

3. Some expressions are unclear, e.g.

L54: “……. more so than local neuropathological processes”.

L91: “ and to a lesser extent in MCI, found using a graph theory approach ”

Reviewer #2: The study used multimodal MRI data including DWI and resting state MRI to compared the difference between MCI and AD. This study has some strengths, such as the advanced analytical method. But there are still several important details need to be clarified.

1. The concept of preclinical stage of AD is definitely not MCI. Cited from a review (Epelbaum et al., 2017), three main issues concerning the concept of preclinical AD must be clarified: 1) the level of cognitive performance considered as normal cognition, 2) the changes in cognitive performance considered as cognitive decline, and 3) the best biomarkers or the best combination of them able to identify the “AD pathophysiological signature” in vivo. Hence, the stage of MCI should be prodromal AD, but not preclinical. Please clarify all related content.

Ref: Epelbaum S, Genthon R, Cavedo E, et al. Preclinical Alzheimer's disease: A systematic review of the cohorts underlying the concept [J]. Alzheimers Dement, 2017, 13(4): 454-67.

2. In the section of Introduction, I think it would have been better to propose the research aims at the last paragraph after review all the related studies and their limitation.

3. In line 142, the cut-off score in GDS is 5?

4. In Line 275-277,” We chose not to include depression as a control covariate given that its presence may reflect dementia pathology rather than a reaction to memory problems.” The relationship between depression and cognitive function is very complicated, part of the depressive elderly would never convert to dementia. This sentence is confusing. Please clarify and revise related analysis result.

5. The author should clarify the definition of DMN-a, -b and -c, because it seems very confusing for a clinician reading.

6. Due to the small sample size, I think it is better to compare with age, gender matched controls. In addition, the age distribution was “54-80”. For neuroimage study, especially AD-related study, age is a very important factor.

7. About method, there are some significant difference between group in neuropsychological test, such as MMSE, word delay test, so what’s the relationship between the neuroimage biomarker with these clinical data. Please do some further correlation analysis which may reflect the clinical implication.

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Reviewer #1: No

Reviewer #2: Yes: Ling Yue

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21 Aug 2019

Reviewer #1:

This paper investigated changes in functional and structural connectivity among regions that support memory function in preclinical Alzheimer’s disease. Then investigated the relationship between white matter tracts (fornix, cingulum bundles, and uncinate fasciculus) and functional connectivity. It’s very interesting and meaningful. But there are some issues to be resolved as following.

Overall Reply: We thank the reviewer for their comments, and their appreciation and help in producing the improvements made to the manuscript.

Main concerns:

1. L313: “Cost (sparsity) was set at 0.15”. How about other costs? Are the results similar or seriously different?

Thank you for this feedback. Given our start of 0.15 we chose to investigate this further using the additional levels: 0.10, 0.20, and 0.25.

We have inserted the following text within the results section:

Given the challenges of defining a-priori an appropriate network cost level (94), and following feedback, we additionally inspected the graph theory measures at sparsity levels of .10, .20 and .25. There were significant differences between the two groups in the cluster coefficient measure for sparsity levels of 0.10, 0.20 and 0.25 (uncorrected for multiple comparisons across levels). As per cost level 0.15, the cluster coefficient measures were higher in the HC compared to the MCI group. At level 0.10 the difference was driven by 16 ROIs (12 from the DMN and hippocampus – 10 of which drive the group differences at sparsity level 0.15). At level 0.20 no individual ROI survived correction for multiple comparisons. At level 0.25 the difference was driven exclusively by LH temporal area 3 of the DMN_b (it was also a driver of differences at levels 0.10 and 0.15). Full details are provided in S1 file. There was a significant difference in the average path length between the groups at sparsity level of 0.10. As per sparsity level 0.15, this average path length difference was not present at levels 0.20 and 0.25. Further details are provided in S1 file.

We have added the below highlighted piece of text in the results section:

The whole-brain graph theory measures revealed that the areas of difference between the two groups were centred on DMN nodes and the hippocampus. The analysis of the cluster coefficient (how well specialist information is segregated) showed that across both hemispheres there were fewer connections-between-nearest-neighbours of select DMN-a and DMN-b nodes and of the hippocampus in the MCI group, at cost levels of 0.10 and 0.15. This metric, related to the resilience of local networks, suggests that these areas in the MCI group are relatively more exposed to insult (114,115). The presence of the hippocampus and DMN-a in the results from both analyses (functional connectivity strength and graph theory) may indicate that it has both lost connections and that its remaining connections are also weaker.

We have added the following highlighted pieces of text to the Limitations section:

Constraints regarding the interpretation of graph theory analysis include those mentioned above for rsMRI (excluding the region of interest approach) and are extended by its binarisation process (119). In order to achieve a high-level overview, it meant that in this instance, only a restricted range of cost % of connections based on correlation strength were included in the analysis. With this reductive approach valuable information is lost and the risk of a skewed understanding of clinically important brain connectivity differences is increased (118). It should also be noted that a widespread difference in underlying functional connectivity between patient and control groups may introduce potential artifactual differences in network topology metrics (120).

We have inserted the following details within the supplementary S1 file:

Table H: Graph theory cluster coefficient, sparsity level 0.10

Measure HC MCI Estimate Statistic P-value*

LH

DMN-b Temp 3 0.558 ± 0.058 0.460 ± 0.100 0.10 4.25 0.000114

DMN-a PCC 1 0.575 ± 0.079 0.490 ± 0.082 0.10 4.14 0.000161

DMN-a PCC 2 0.555 ± 0.054 0.496 ± 0.075 0.07 3.70 0.000608

SomMot A 16 0.531 ± 0.0 0.423 ± 0.094 0.11 3.60 0.000808

DMN-b PFCd 1 0.530 ± 0.071 0.453 ± 0.084 0.08 3.58 0.000871

Hippocampus 0.498 ± 0.126 0.369 ± 0.132 0.14 3.49 0.001137

DMN-a PFCm 3 0.561 ± 0.077 0.483 ± 0.094 0.09 3.47 0.001198

DMN-b Temp 2 0.568 ± 0.089 0.478 ± 0.097 0.09 3.33 0.001781

RH

Hippocampus 0.527 ± 0.125 0.357 ± 0.136 0.20 5.53 0.000002

DMN-a PFCm 1 0.536 ± 0.068 0.450 ± 0.080 0.09 4.12 0.000170

DMN-b PFCd 4 0.555 ± 0.076 0.475 ± 0.107 0.10 3.72 0.000572

Limibic-a OFC 6 0.553 ± 0.079 0.453 ± 0.112 0.11 3.60 0.000818

SalVentAttn A ParMed 8 0.500 ± 0.101 0.426 ± 0.102 0.10 3.49 0.001124

SalVentAttn A ParMed 4 0.487 ± 0.094 0.399 ± 0.090 0.10 3.46 0.001235

DMN-a PFCd 2 0.555 ± 0.078 0.463 ± 0.095 0.09 3.45 0.001268

DMN-a PCC 1 0.559 ± 0.064 0.494 ± 0.077 0.07 3.36 0.001649

Graph theory analysis, at sparsity level 0.10, revealed a significant between-group difference in the cluster coefficient measure. This was higher in the HC (M=0.484, SD=0.027) compared to the MCI (M=0.461, SD=0.035) group (b = .03, t = 2.90, puncor = .006). See table H above.

Graph theory analysis, at sparsity level 0.20, revealed a significant between-group difference in the cluster coefficient measure. This was higher in the HC (M=0.504, SD=0.025) compared to the MCI (M=0.490, SD=0.031) group (b = .02, t = 2.31, puncor = .026). The ROIs driving this difference did not survive correction for multiple comparisons.

Graph theory analysis, at sparsity level 0.25, revealed a significant between-group difference in the cluster coefficient measure. This was higher in the HC (M=0.521, SD=0.024) compared to the MCI (M=0.509, SD=0.029) group (b = .02, t = 2.19, puncor = .034). See table I.

Table I: Graph theory cluster coefficient, sparsity level 0.25

Measure HC MCI Estimate Statistic P-value*

LH

DMN-b Temp 3 0.580 ± 0.051 0.512 ± 0.070 0.08 4.69 0.00028

Graph theory analysis, at sparsity level 0.10, revealed a statistical difference in average path length between the groups, with a longer path length in HC compared to controls (b = .05, t = 2.42, puncor = .020; HC: M = 2.39, SD = 0.071; MCI: M = 2.35, SD = 0.084). This difference was driven by two ROIs in the somatomotor network - one in the left hemisphere and one in the right.

Table J: Graph theory average path length, sparsity level 0.10

Measure HC MCI Estimate Statistic P-value*

LH

SomMot-a 15 2.46 ± 0.215 2.22. ± 0.180 0.25 4.25 0.000112

RH

SomMot-a 13 2.45 ± 0.129 2.25 ± 0.200 0.19 4.20 0.000133

Graph theory analysis did not reveal a stastical difference in average path length between the two groups at sparsity level 0.20: (b = .02, t = 1.54, puncor = .130; HC: M = 1.89, SD = 0.035; MCI: M = 1.88, SD = 0.040), nor at sparsity level 0.25: (b = .01, t = 1.11, puncor = .275; HC: M = 1.78, SD = 0.024; MCI: M = 1.78, SD = 0.029).

2. L292: “Within hemisphere only analyses were conducted, across hemisphere connectivity was not inspected due to sample size”. It’s better and possible to make a comparison across hemisphere because authors didn’t use multivariate.

We agree with the reviewer that such comparisons of functional connectivity are of interest. However, we were confronted with the statistical limitations of our sample size. With seven regions of interest per hemisphere, it would require 91 comparisons to inspect all possible pairings within and across hemispheres. This, alongside the requisite correction for multiple comparisons, was deemed a-priori to be beyond the capacity of this sample’s data.

It is also worth highlighting that analysis of the limbic white matter revealed differences between the hemispheres. However, because the focus was on limbic white matter tracts we did not analyse the corpus callosum. The aforementioned statistical considerations aside, these points taken together, would complicate any interpretation of across hemisphere functional relationships (if we had found them).

3. L272: “Its lmrob function fits a model based on an M-estimator using iteratively reweighted least squares estimation”. Could you list the model equation in details?

Here is an indicative example of how the formula was run in R:

lmrob(Mean_FA ~ Group + Age + Gender + Education, data = L_)

This approach was chosen because it is more appropriate for small sample sizes and the attendant problems of meeting test assumptions, including the issue of having a large number of parameters or covariates to model relative to the sample size, as per this study. This approach was chosen based on the recommendation of Field and Wilcox (2017):

“M-estimators determine whether a score is an outlier empirically and if it is, adjustments are made for it. The adjustment could be to completely ignore the observation or to down-weight it. Obvious advantages of M-estimators are that you can (1) down-weight rather than exclude observations; (2) avoid over- or under-trimming your data; and (3) perform non-symmetric trimming.” (p 23, 1)

1. Field AP, Wilcox RR. Robust statistical methods: A primer for clinical psychology and experimental psychopathology researchers. Behav Res Ther [Internet]. 2017;98:19–38. Available from: http://dx.doi.org/10.1016/j.brat.2017.05.013

“This function computes an MM-type regression estimator as described in Yohai (1987) and Koller and Stahel (2011). By default it uses a bi-square redescending score function, and it returns a highly robust and highly efficient estimator (with 50% breakdown point and 95% asymptotic efficiency for normal errors).” (p 67, 2)

2. Maecheler M, Rousseeuw P, Croux C, Todorov V, Rucksuhl A, Salibian-Barrera M, et al. “robustbase”: Basic robust statistics. R package [Internet]. 2018. Available from: http://cran.r-project.org/package=robustbase

A number of formulae are required to calculate the robust regression. With due deference to the field of the statistics, we refer the reviewer to the source materials for a fuller appreciation of the approach:

3. Koller M, Stahel WA. Sharpening Wald-type inference in robust regression for small samples. Comput Stat Data Anal [Internet]. 2011;55(8):2504–15. Available from: http://dx.doi.org/10.1016/j.csda.2011.02.014

4. Yohai, V., 1987. High breakdown-point and high efficiency robust estimates for regression. The Annals of Statistics 15 (2), 642–656.

Minor concerns:

1. the figures aren’t with enough resolution, especially the title and number are unclear.

Thank you for highlighting this frustration. We followed the guidelines provided and used the recommended PACE website to ensure the figures met publication standards. Should the paper be accepted, perhaps this is something we can work with the publication team to resolve?

2. Table 3,4, why t(43), what does 43 mean? And the table format should be revised for consistency.

t(43) refers to the t-statistic for 43 degrees of freedom (allowing for 2 groups, with age, gender and education as control covariates). As suggested, we have changed the text to ‘statistic’ so that it is consistent with the other tables.

3. Some expressions are unclear, e.g.

L54: “……. more so than local neuropathological processes”.

We have revised the sentence to improve clarity:

“For example, it is proposed that early hypometabolism seen in the posterior cingulate cortex reflects distant damage in the hippocampal formation more so than local neuropathological processes within the posterior cingulate cortex (9,10).”

L91: “ and to a lesser extent in MCI, found using a graph theory approach ”

We have revised the sentence to improve clarity:

“That finding of increased functional connectivity parallels the lack of segregation between the DMN and frontoparietal networks that have been revealed using a graph theory approach in AD, and to a lesser extent, MCI patients.”

Reviewer #2:

The study used multimodal MRI data including DWI and resting state MRI to compared the difference between MCI and AD. This study has some strengths, such as the advanced analytical method. But there are still several important details need to be clarified.

Overall Reply: We thank Dr Yue for their comments, and their appreciation and help in producing the improvements made to the manuscript.

For the sake of avoiding confusion in the below responses (in case the paper is not to hand), we wish to clarify that the study compared differences between a group with MCI and a matched healthy control group.

1. The concept of preclinical stage of AD is definitely not MCI. Cited from a review (Epelbaum et al., 2017), three main issues concerning the concept of preclinical AD must be clarified: 1) the level of cognitive performance considered as normal cognition, 2) the changes in cognitive performance considered as cognitive decline, and 3) the best biomarkers or the best combination of them able to identify the “AD pathophysiological signature” in vivo. Hence, the stage of MCI should be prodromal AD, but not preclinical. Please clarify all related content.

Ref: Epelbaum S, Genthon R, Cavedo E, et al. Preclinical Alzheimer's disease: A systematic review of the cohorts underlying the concept [J]. Alzheimers Dement, 2017, 13(4): 454-67.

Thank you for pointing this out and for providing the relevant reference. We have removed the two references to preclinical AD, in the title and in the abstract (line 15), and replaced with prodromal AD:

Title: “No relationship between fornix and cingulum degradation and within-network decreases in functional connectivity in prodromal Alzheimer’s disease”

Line 15: “We investigated changes in functional and structural connectivity among regions that support memory function in prodromal Alzheimer’s disease, i.e., during the mild cognitive impairment (MCI) stage.”

2. In the section of Introduction, I think it would have been better to propose the research aims at the last paragraph after review all the related studies and their limitation.

We agree that is helpful to place the purpose of the study at the end of the introduction section. We had previously received feedback from colleagues urging us to provide context in the initial paragraph of the introduction. It seems to be an important point for the reader. For these reasons we attempted to address both viewpoints by mentioning aims in both the first and last paragraph of the introduction:

First paragraph of introduction:

“This study aims to probe the relationship between structural and functional brain changes in MCI through use of resting state magnetic resonance imaging (rsMRI) and diffusion weighted imaging (DWI).”

Last paragraph of introduction:

“This study adds to the literature by investigating constrained spherical deconvolution DWI measures (54) of MCI-targeted white matter tracts (fornix, cingulum bundles, and uncinate fasciculus) and temporal correlation connectivity measures of MCI-targeted functional networks (DMN and limbic), and by examining the relationship between those structural and functional measures.”

Admittedly this approach is unlikely to be agreeable to all stylistic perspectives. However, we hope it does not deter from the overall readability and understanding of the paper and its aims.

3. In line 142, the cut-off score in GDS is 5?

Thank you for picking this up. We used the short-form of the GDS, it has a suggested cut-off of 5. We have inserted ‘short-form’ at the first mention of the use of the GDS in our study, in line 151:

‘……short-form Geriatric Depression Scale (GDS; (59))’

4. In Line 275-277,” We chose not to include depression as a control covariate given that its presence may reflect dementia pathology rather than a reaction to memory problems.” The relationship between depression and cognitive function is very complicated, part of the depressive elderly would never convert to dementia. This sentence is confusing. Please clarify and revise related analysis result.

Thank you for bringing our attention to this section of text. We have inserted the below text within the analysis section. The text now emphasizes the complication of depression and provides more clarity around the reasons for our analysis choice. Now line 299-304:

The relationship between depression and cognitive function is complicated and some depressive elderly will not convert to dementia (84). However, we chose not to include depression as a control covariate given that its presence may reflect dementia pathology (85). That is, controlling for depression would run the risk of removing relevant explanatory variance. Further, in this group it would both reduce sample size as one participant declined to complete the GDS and risk over-fitting the model. In any case, in this cohort depression did not correlate with worsening measures of cognition (see Fig B in S1 file).

Further, we have clarified the details on missing neuropsychological information in the methods section:

“Five MCI people declined to complete the entirety of the neuropsychological testing set (four did not complete the CERAD tests, one did not complete the CERAD or GDS tests) – however, all were successfully scanned.”

5. The author should clarify the definition of DMN-a, -b and -c, because it seems very confusing for a clinician reading.

Thank you for sharing this perspective. The atlas we used (Schaefer et al, 2018) features a relatively recent and specific computational-based segmentation of the default mode network based on functional connectivity.

These parcellations were defined using task and resting state fMRI data of 1489 young adults (18-35 years old) and a computational method that made the optimal combination of two different segmentation approaches: 1) abrupt changes in functional connectivity patterns such as might be defined histologically and 2) parcels of similar functional activity identified independently of spatial considerations. This parcellation method is thought to reflect functional brain organization in a neurobiologically meaningful way.

Alongside creating functional anatomical clusters, the parcellations are helpful from a research and statistical perspective (it moves on from examining every single voxel in a MRI dataset). In order to address different research needs, the authors provided parcellations in varying amounts of clusters (400, 600, 800, 1000), i.e., from diffuse to focal levels. In our study we chose the atlas with 400 parcellations. In that instance it revealed a default mode network that could be meaningfully sub-divided into 3 functional clusters, and a limbic network that could be divided into two functional clusters. Within each of these clusters focal sources of the functional activity can be further labelled. In figure 2 of the paper, we provided a view of the focal sources within each cluster of the default mode and limbic networks.

We have amended the text as follows:

Using the 2mm 400 region cortical atlas (80) two limbic (a and b) and three default mode sub-networks (a, b, and c) per hemisphere were identified. These atlas parcellations were computed from functional connectivity patterns. The sources of the functional connectivity signals within each sub-network are detailed in Fig 2. Centroid co-ordinates for the parcels are presented in S1 file table L.

Due to its length, table L has been placed at the end of the present document.

Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo X, Holmes AJ, et al. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb Cortex. 2018;28:3095–114.

6. Due to the small sample size, I think it is better to compare with age, gender matched controls. In addition, the age distribution was “54-80”. For neuroimage study, especially AD-related study, age is a very important factor.

We agree with the reviewer that age is an important consideration. While the healthy control group is unfortunately not a precise like-for-like of the MCI group, there were no statistically significant demographic differences between the two groups. We also took the step of including age, gender and education as control covariates in the linear regressions.

Taking the opportunity to reflect on the reviewer’s comment regarding the sample more generally, we have included an additional sentence (L504) to highlight the possibility that the lack of finding of a relationship between functional and structural connectivity is due to the sample size constraints:

“It is also possible that, notwithstanding the simple statistical approach we used, the sample size is simply too small (see limitations section).”

7. About method, there are some significant difference between group in neuropsychological test, such as MMSE, word delay test, so what’s the relationship between the neuroimage biomarker with these clinical data. Please do some further correlation analysis which may reflect the clinical implication.

Thank you for raising this point, we do appreciate that such an analysis could hold interest. We chose not to explore this aspect primarily because it falls outside the focus of the paper. It also risks ending up reporting spurious relations due to statistical limitations. There are a couple of reasons for this. First, there is the potential to investigate multiple relationships – there are 50 metrics for the DWI data and 52 for the functional connectivity data that could each be investigated for relationships with up to 11 neuropsychological measures. Even limiting the analysis to a post-hoc exploration between those imaging and neuropsychological measures that were significantly different between groups would still require 120 tests. Second, as mentioned above, five MCI patients have incomplete neuropsychological test sets thus reducing the sample size available for such correlations to twenty patients. For these reasons, albeit that we appreciate the rationale for the suggestion, we would prefer not to undertake those analyses. The data is being made publicly available and we sincerely hope that it might be of use to other researchers who may be in a position to combine it with other datasets to create the statistical bandwidth to answer such questions.

Reviewer #2, point 5: Additional information for the supplementary file:

Table L: Schaefer atlas 2018 400 parcels 17 networks MNI152 2mm – centroid co-ordinates for default mode and limbic networks. Elements shaded in grey were implicated in one/more graph theory analyses. For complete list of centroids see: https://github.com/ThomasYeoLab/CBIG/blob/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations/MNI/Centroid_coordinates/Schaefer2018_400Parcels_17Networks_order_FSLMNI152_2mm.Centroid_RAS.csv

ROI Index Label Name X Y Z

36 17Networks_LH_SomMotA_12 -24 -10 64

39 17Networks_LH_SomMotA_15 -4 -26 68

40 17Networks_LH_SomMotA_16 -14 -12 72

109 17Networks_LH_Limbic_OFC_1 -12 24 -20

110 17Networks_LH_Limbic_OFC_2 -24 22 -20

111 17Networks_LH_Limbic_OFC_3 -10 48 -22

112 17Networks_LH_Limbic_OFC_4 -4 24 -20

113 17Networks_LH_Limbic_OFC_5 -16 64 -8

114 17Networks_LH_Limbic_TempPole_1 -38 -6 -42

115 17Networks_LH_Limbic_TempPole_2 -24 6 -40

116 17Networks_LH_Limbic_TempPole_3 -26 -10 -32

117 17Networks_LH_Limbic_TempPole_4 -54 -22 -30

118 17Networks_LH_Limbic_TempPole_5 -40 -22 -26

119 17Networks_LH_Limbic_TempPole_6 -32 12 -30

120 17Networks_LH_Limbic_TempPole_7 -44 6 -16

149 17Networks_LH_DefaultA_IPL_1 -48 -64 32

150 17Networks_LH_DefaultA_IPL_2 -42 -72 44

151 17Networks_LH_DefaultA_PFCd_1 -24 28 44

152 17Networks_LH_DefaultA_PFCd_2 -18 36 48

153 17Networks_LH_DefaultA_PFCd_3 -22 20 52

154 17Networks_LH_DefaultA_PCC_1 -4 -54 20

155 17Networks_LH_DefaultA_PCC_2 -6 -60 30

156 17Networks_LH_DefaultA_PCC_3 -8 -44 32

157 17Networks_LH_DefaultA_PCC_4 -4 -34 38

158 17Networks_LH_DefaultA_PCC_5 -2 -16 38

159 17Networks_LH_DefaultA_PCC_6 -2 -68 42

160 17Networks_LH_DefaultA_PCC_7 -6 -50 42

161 17Networks_LH_DefaultA_PFCm_1 -4 56 -10

162 17Networks_LH_DefaultA_PFCm_2 -6 36 -8

163 17Networks_LH_DefaultA_PFCm_3 -6 60 6

164 17Networks_LH_DefaultA_PFCm_4 -6 44 6

165 17Networks_LH_DefaultA_PFCm_5 -16 68 8

166 17Networks_LH_DefaultA_PFCm_6 -6 34 20

167 17Networks_LH_DefaultB_Temp_1 -44 12 -34

168 17Networks_LH_DefaultB_Temp_2 -54 -2 -30

169 17Networks_LH_DefaultB_Temp_3 -62 -18 -20

170 17Networks_LH_DefaultB_Temp_4 -56 -8 -14

171 17Networks_LH_DefaultB_Temp_5 -60 -34 -4

172 17Networks_LH_DefaultB_Temp_6 -52 -22 -6

173 17Networks_LH_DefaultB_IPL_1 -46 -58 20

174 17Networks_LH_DefaultB_IPL_2 -56 -54 30

175 17Networks_LH_DefaultB_PFCd_1 -4 52 28

176 17Networks_LH_DefaultB_PFCd_2 -14 58 30

177 17Networks_LH_DefaultB_PFCd_3 -22 50 32

178 17Networks_LH_DefaultB_PFCd_4 -8 42 52

179 17Networks_LH_DefaultB_PFCd_5 -12 24 60

180 17Networks_LH_DefaultB_PFCd_6 -6 10 64

181 17Networks_LH_DefaultB_PFCl_1 -40 20 48

182 17Networks_LH_DefaultB_PFCl_2 -42 8 48

183 17Networks_LH_DefaultB_PFCv_1 -36 22 -16

184 17Networks_LH_DefaultB_PFCv_2 -36 36 -12

185 17Networks_LH_DefaultB_PFCv_3 -46 32 -10

186 17Networks_LH_DefaultB_PFCv_4 -48 28 0

187 17Networks_LH_DefaultB_PFCv_5 -54 20 12

188 17Networks_LH_DefaultC_IPL_1 -40 -78 30

189 17Networks_LH_DefaultC_Rsp_1 -14 -48 4

190 17Networks_LH_DefaultC_Rsp_2 -8 -52 10

191 17Networks_LH_DefaultC_Rsp_3 -14 -60 18

192 17Networks_LH_DefaultC_PHC_1 -20 -20 -26

193 17Networks_LH_DefaultC_PHC_2 -30 -32 -18

194 17Networks_LH_DefaultC_PHC_3 -18 -38 -12

236 17Networks_RH_SomMotA_13 10 -40 68

299 17Networks_RH_SalVentAttnA_ParMed_4 6 10 58

302 17Networks_RH_SalVentAttnA_ParMed_7 6 -2 66

303 17Networks_RH_SalVentAttnA_ParMed_8 16 6 70

313 17Networks_RH_Limbic_OFC_1 14 24 -20

314 17Networks_RH_Limbic_OFC_2 22 22 -20

315 17Networks_RH_Limbic_OFC_3 8 46 -24

316 17Networks_RH_Limbic_OFC_4 20 42 -18

317 17Networks_RH_Limbic_OFC_5 4 22 -20

318 17Networks_RH_Limbic_OFC_6 10 62 -14

319 17Networks_RH_Limbic_TempPole_1 28 -2 -40

320 17Networks_RH_Limbic_TempPole_2 48 -6 -40

321 17Networks_RH_Limbic_TempPole_3 36 18 -38

322 17Networks_RH_Limbic_TempPole_4 40 -14 -32

323 17Networks_RH_Limbic_TempPole_5 28 12 -30

324 17Networks_RH_Limbic_TempPole_6 50 -28 -26

358 17Networks_RH_DefaultA_Temp_1 60 -8 -24

359 17Networks_RH_DefaultA_IPL_1 54 -54 26

360 17Networks_RH_DefaultA_IPL_2 48 -64 42

361 17Networks_RH_DefaultA_PFCd_1 26 34 38

362 17Networks_RH_DefaultA_PFCd_2 24 26 50

363 17Networks_RH_DefaultA_PCC_1 6 -52 24

364 17Networks_RH_DefaultA_PCC_2 4 -64 32

365 17Networks_RH_DefaultA_PCC_3 6 -38 34

366 17Networks_RH_DefaultA_PCC_4 4 -20 36

367 17Networks_RH_DefaultA_PCC_5 10 -52 36

368 17Networks_RH_DefaultA_PFCm_1 6 42 -10

369 17Networks_RH_DefaultA_PFCm_2 10 66 0

370 17Networks_RH_DefaultA_PFCm_3 8 42 4

371 17Networks_RH_DefaultA_PFCm_4 8 54 12

372 17Networks_RH_DefaultA_PFCm_5 18 64 16

373 17Networks_RH_DefaultA_PFCm_6 6 26 18

374 17Networks_RH_DefaultB_Temp_1 64 -24 -8

375 17Networks_RH_DefaultB_Temp_2 64 -38 0

376 17Networks_RH_DefaultB_AntTemp_1 50 8 -32

377 17Networks_RH_DefaultB_PFCd_1 6 58 30

378 17Networks_RH_DefaultB_PFCd_2 16 52 36

379 17Networks_RH_DefaultB_PFCd_3 4 44 40

380 17Networks_RH_DefaultB_PFCd_4 14 38 52

381 17Networks_RH_DefaultB_PFCd_5 12 20 62

382 17Networks_RH_DefaultB_PFCv_1 34 22 -18

383 17Networks_RH_DefaultB_PFCv_2 48 32 -8

384 17Networks_RH_DefaultB_PFCv_3 54 24 6

385 17Networks_RH_DefaultC_IPL_1 48 -64 22

386 17Networks_RH_DefaultC_IPL_2 46 -76 30

387 17Networks_RH_DefaultC_Rsp_1 14 -46 4

388 17Networks_RH_DefaultC_Rsp_2 12 -56 16

389 17Networks_RH_DefaultC_PHC_1 22 -18 -28

390 17Networks_RH_DefaultC_PHC_2 30 -30 -18

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

5 Sep 2019

[EXSCINDED]

PONE-D-19-16445R1

No relationship between fornix and cingulum degradation and within-network decreases in functional connectivity in prodromal Alzheimer’s disease

PLOS ONE

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Please address the reviewer #1's minor concern.

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Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: Minor concerns:

The sign of static value in different tables may not be consistent. For example, in table 2, positive statistic value means increase in MCI subjects, negative means decrease. But in table 3, they show opposite meaning according to the results description.

The statistic value means T value? It's better to be clearer in each table.

Reviewer #2: The authors addressed all the comments adequately, and their manuscript is significantly improved. I have no further comments.

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11 Sep 2019

We thank both reviewers for again spending time and giving great attention to improving the paper. We are appreciative of your support.

Reviewer #1: Minor concerns:

The sign of static value in different tables may not be consistent. For example, in table 2, positive statistic value means increase in MCI subjects, negative means decrease. But in table 3, they show opposite meaning according to the results description.

The statistic value means T value? It's better to be clearer in each table.

Reply: You are correct. Differences in how the reference group was defined in the various analyses files are behind these inconsistencies. However, as you point out the explanation for said differences had been correctly described in the manuscript.

We have amended the text for the DWI results and its corresponding Table 2 in the main manuscript:

All diffusion measures of the fornix showed evidence of degeneration in the MCI group: MD, Da and Dr were comparatively increased in the left (MD: t = 4.21, pcor = .0006, Da: t = 5.32, pcor = .00005, Dr: t = 3.74, pcor = .002) and the right fornix (MD: t = 5.06, pcor < .0001, Da: t = 5.59, pcor = .00005, Dr: t = 4.65, pcor = .0002), and FA was comparatively decreased in the left (t = -2.78, pcor = .019) and right (t = -2.42, pcor = .035) fornices. In the MCI compared to the HC group, MD and Da were significantly increased in the left parahippocampal cingulum (MD: t = 2.72, pcor = .019, Da: t = 3.02, pcor = .012), and MD and Dr were significantly increased in the left retrosplenial cingulum (MD: t = 3.08, pcor = .011, Dr: t = 2.43, pcor = .035) and in the left subgenual cingulum (MD: t = 3.08, pcor = .011, Dr: t = 2.77, pcor = .019).

Table 3. MD values per structure per hemisphere

Structure Hemi HC MCI Estimate t-statistic p-value *

Fornix LH 0.00120 ± 0.00008 0.00132 ± 0.00014 0.000139 4.210 0.000064

RH 0.00118 ± 0.00008 0.00127 ± 0.00008 0.000100 5.056 .0000042

Para-hippocampal cingulum LH 0.00072 ± 0.00003 0.00075 ± 0.00005 0.0000187 2.721 0.00467

RH 0.00072 ± 0.00003 0.00074 ± 0.00005 0.0000104 1.007 0.15950

Retrosplenial cingulum LH 0.00067 ± 0.00002 0.00069 ± 0.00002 0.0000213 3.084 0.00178

RH 0.00067 ± 0.00002 0.00068 ± 0.00003 0.0000148 2.052 0.02315

Subgenual cingulum LH 0.00069 ± 0.00002 0.00070 ± 0.00002 0.00001516 3.078 0.00181

RH 0.00069 ± 0.00002 0.00069 ± 0.00002 0.00000946 1.706 0.04765

Uncinate fasciculus LH 0.00070 ± 0.00002 0.00071 ± 0.00004 0.00000444 0.806 0.21225

RH 0.00072 ± 0.00002 0.00072 ± 0.00003 0.00000254 0.336 0.36900

Hemi = hemisphere, LH = left hemisphere, RH = right hemisphere. *p-values are uncorrected, 1-sided

We have corrected the text for the rsMRI results (tables were already correct):

In MCI compared to HC, statistically smaller within-network connectivity was found in the DMN-a in the left (t = -3.38, pcor = .010) and right (t = -3.75, pcor = .005) hemispheres. See Table 4.

In MCI compared to HC, statistically smaller between-network connectivity was found between DMN-a and DMN-c, between DMN-a and the hippocampus, and between DMN-c and the hippocampus in both the left hemisphere (respectively: t = -4.63, pcor = .002; t = -4.21, pcor = .003; t = -3.99, pcor = .003) and the right hemisphere (respectively: t = -3.81, pcor = .005; t = -3.31, pcor = .011; t = -3.52, pcor = .008). In the right hemisphere only in MCI compared to HC, statistically smaller between-network connectivity was found between DMN-c and the thalamus (t = -3.02, pcor = .021) and between DMN-c and the limbic-a network (t = -4.01, pcor = .003). See Table 5, and Figs 5 and 6, for results mentioned here and Table G in S1 file for the complete set.

We have amended text and table for the graph theory results:

Graph theory analyses, at sparsity level of 0.15, revealed a significant between-group difference in the cluster coefficient measure. This was higher in the HC (M=0.492, SD=0.027) compared to the MCI (M=0.474, SD=0.033) group (b = .02, t = -2.53, puncor = .015). This difference was driven by 18 ROIs - 8 in DMN-a (5 RH, 3 LH), 5 in DMN-b (2 RH, 3LH), both hippocampi, two ROIs in the LH somatomotor network and one in the RH salient ventral attention network – all of which survived FDR-correction. See Fig 7 and Table 6 for details. The difference in average path length between the two groups did not reach statistical significance (b = .03, t = -1.94, puncor = .059; HC: M=2.08, SD=0.049; MCI: M=2.06, SD=0.06).

Table 6. Graph theory cluster coefficient, sparsity level of 0.15

Measure HC MCI Estimate t-statistic P-value*

LH

DMN-a PCC 2 0.569 ± 0.045 0.510 ± 0.067 0.07 -4.17 0.000146

DMN-b Temp 3 0.564 ± 0.054 0.481 ± 0.096 0.09 -3.98 0.000262

DMN-b PFCd 5 0.540 ± 0.087 0.454 ± 0.089 0.10 -3.79 0.000458

DMN-a PFCm 1 0.536 ± 0.065 0.471 ± 0.066 0.07 -3.65 0.000713

DMN-a PFCd 3 0.525 ± 0.073 0.469 ± 0.071 0.07 -3.53 0.001002

DMN-b PFCd 1 0.543 ± 0.065 0.471 ± 0.075 0.08 -3.49 0.001117

SomMoTA 16 0.546 ± 0.089 0.458 ± 0.104 0.10 -3.42 0.001383

Hippocampus 0.501 ± 0.094 0.429 ± 0.081 0.09 -3.35 0.001687

SomMoTA 12 0.544 ± 0.089 0.466 ± 0.089 0.09 -3.29 0.002004

RH

DMN-a PFCm 1 0.532 ± 0.062 0.451 ± 0.071 0.09 -4.77 0.000022

DMN-a PFCd 2 0.559 ± 0.061 0.466 ± 0.080 0.10 -4.46 0.000058

Hippocampus 0.514 ± 0.123 0.421 ± 0.093 0.11 -3.78 0.000472

DMN-a PFCm 4 0.542 ± 0.062 0.477 ± 0.071 0.07 -3.52 0.001023

DMN-b Ant Temp 1 0.541 ± 0.063 0.467 ± 0.096 0.09 -3.51 0.001052

DMN-b PFCd 4 0.558 ± 0.066 0.488 ± 0.101 0.09 -3.46 0.001215

DMN-a PCC 1 0.573 ± 0.057 0.512 ± 0.069 0.07 -3.46 0.001222

DMN-a PFCm 3 0.507 ± 0.086 0.452 ± 0.075 0.08 -3.43 0.001335

SalVentAttn A ParMed7 0.497 ± 0.067 0.437 ± 0.057 0.06 -3.34 0.001735

Any inconsistencies in the S1 file have also been rectified.

As recommended, we have replaced the column header ‘statistic’ with ‘t-statistic’ in the relevant tables throughout the main manuscript and the S1 file.

Reviewer #2:

The authors addressed all the comments adequately, and their manuscript is significantly improved. I have no further comments.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

12 Sep 2019

No relationship between fornix and cingulum degradation and within-network decreases in functional connectivity in prodromal Alzheimer’s disease

PONE-D-19-16445R2

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25 Sep 2019

PONE-D-19-16445R2

No relationship between fornix and cingulum degradation and within-network decreases in functional connectivity in prodromal Alzheimer’s disease

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