Brain responsivity to emotional faces differs in men and women with and without a history of alcohol use disorder.

Inclusion of women in research on Alcohol Use Disorder (AUD) has shown that gender differences contribute to unique profiles of cognitive, emotional, and neuropsychological dysfunction. We employed functional magnetic resonance imaging (fMRI) of abstinent individuals with a history of AUD (21 women [AUDw], 21 men [AUDm]) and demographically similar non-AUD control (NC) participants without AUD (21 women [NCw], 21 men [NCm]) to explore how gender and AUD interact to influence brain responses during emotional processing and memory. Participants completed a delayed match-to-sample emotional face memory fMRI task, and brain activation contrasts between a fixation stimulus and pictures of emotional face elicited a similar overall pattern of activation for all four groups. Significant Group by Gender interactions revealed two activation clusters. A cluster in an anterior portion of the middle and superior temporal gyrus, elicited lower activation to the fixation stimulus than to faces for the AUDw as compared to the NCw; that abnormality was more pronounced than the one observed for men. Another cluster in the medial portion of the superior frontal cortex elicited higher activation to the faces by AUDm than NCm, a difference that was more evident than the one observed for women. Together, these findings have added new evidence of AUD-related gender differences in neural responses to facial expressions of emotion.

Introduction

Chronic long-term Alcohol Use Disorder (AUD), also referred to as “alcoholism,” is a harmful condition [1, 2] that has been associated with deficits in emotion and memory, including memory for the emotional expressions of faces [3-6]. In addition to its effects on memory for facial emotions, AUD also has been associated with impairments in the processing of facial emotional expressions [7-11], and these effects can endure after months of sobriety [12, 13].

Brain regions associated with the encoding of face identity and emotional expressions have been established [14], and research is beginning to indicate how the activity of these regions varies in conjunction with AUD. In the present study, we explored face encoding in particular, which relies upon prefrontal cortex, amygdala, hippocampus, fusiform, and lateral parietal regions [15-17]. Processing of emotional facial expressions and identity appear to be partially functionally segregated [18]. That is, attention to the identity of faces and to the emotions expressed on them, influences the way in which networks are utilized. Attention to face identity typically activates fusiform and inferior temporal areas, and attention to the emotional expression has been shown to activate superior temporal, amygdala, putamen, insula, cingulate, and inferior frontal regions [19-24]. Functional magnetic resonance imaging (fMRI) studies of facial emotion processing among healthy adults have reported activation in a widespread network of brain areas. These areas include the fusiform gyrus, lateral occipital gyrus, superior temporal sulcus, inferior frontal gyrus, insula, orbitofrontal cortex, basal ganglia, and the amygdala [25-28]. These were the amygdala, fusiform gyrus, hippocampus, parahippocampal gyrus, intraparietal sulcus, orbitofrontal cortex, superior temporal gyrus, and superior temporal sulcus.

Facial identity also has been examined in the context of gender [29]. For example, men have been reported to show stronger activation than women in several frontal and temporal brain regions [30, 31], suggesting potential gender dimorphism in the processing of facial emotional expressions. The higher activation of these regions in men could be related to other factors observed to vary with emotion, such as hormones, genetics, and culture [29]. In addition to differentiating how facial emotion is processed, gender also influences the ways in which alcoholism impacts the brain, and further, how emotion, gender, and alcoholism interact.

Historically, AUD has afflicted more men than women, but prevalence for women has increased such that they are approaching similar rates [5, 32–36]. Much of what has been learned about the long-term effects of alcoholism has been based upon research that focused on men, or has not differentiated the results obtained from different genders. There exist clear differences in how alcohol affects men and women cognitively and physiologically [37] and in how they progress from social to problem drinkers [34, 38]. AUD-related gender effects on brain structure also have been described, involving white matter volume [39-42], morphometry of the brain reward system [43], and cerebellar subregional volumes [44]. Additionally, we have reported AUD-related gender-dimorphic effects in multiple functional domains including emotional processing [45], personality [46], and drinking motives [5, 47]. However, functional magnetic resonance imaging (fMRI) studies of AUD-related gender differences in the brain during performance of tasks involving emotion are rare [45].

The present study sought to characterize abnormalities in neural activation among abstinent participants with AUD, through analysis of BOLD responses to photographs of faces that varied in emotional expressions. We were particularly interested in observing fMRI activation in brain regions that subserve face processing, memory encoding, and emotions, and additionally, in characterizing how these effects differ between AUD men (AUDm) and AUD women (AUDw), as compared to non-AUD control men (NCm) and women (NCw). When forming our specific a priori hypothesis with regard to AUD-related gender differences in response to emotional stimuli, we noted that the literature reported mixed findings. Across brain regions, previous literature has described abnormalities in several directions, with women evidencing greater brain activitivation in response to emotional stimuli, men having more brain activity, or having undetectable gender differences [5, 11, 45, 48–50]. Based upon prior research in our laboratory, wherein brain regions of AUDw (compared to NCw) were found to be overactive in response to highly charged emotional stimuli [45], in the present study we hypothesized that the pattern of AUD-related activation abnormalities would differ for men and women, and the brain responses to emotional faces by the AUDw would be hyperactive. Additionally, we expected to replicate prior results [45, 51] showing widespread lower responses in AUDm than NCm.

Methods

Participants

Participants included 42 abstinent individuals with a history of long-term AUD (21 AUDw) and 42 controls (21 NCw) (Table 1). Participants were recruited within the Boston area through advertisements placed in public spaces (e.g., hospitals, churches, stores), newspapers, and internet websites. This research was approved by the Boston Medical Center/Boston University School of Medicine Institutional Review Board (#H24686), VA Boston Healthcare System Institutional Review Board (#1017 and #1018), and the Partners Healthcare System Human Research Institutional Review Board (#2000P001891). Participants provided written informed consent for participation in the study. Participants gave written informed consent prior to participation, and were compensated for their time.

Table 1

Participant characteristics.

	AUD GROUP			CONTROL GROUP
	All	Women	Men	All	Women	Men
	n = 42	n = 21	n = 21	n = 42	n = 21	n = 21
Agea (years)
mean	53.9	53.4	54.4	53.9	57.7	50.2
standard deviation	11.0	11.4	10.8	12.4	13.6	10.1
range	26.5–76.7	26.5–73.0	26.6–76.7	25.8–76.9	25.8–76.9	29.0–69.6
Educationb (years)
mean	14.7	15.3	14.1	15.5	15.6	15.4
standard deviation	2.0	2.0	1.9	2.0	2.3	1.6
range	12–19	12–19	12–18	12–19	12–20	12–18
WAIS-III Full Scale IQ
mean	110.3	110.1	110.5	111.6	111.2	112.0
standard deviation	15.0	14.2	16.0	16.3	19.3	13.1
range	72–140	72–137	81–140	79–152	79–142	90–152
WMS-III IMI
mean	109.7	114.4	104.7	111.9	114.8	109.0
standard deviation	16.6	18.3	13.4	16.9	16.4	17.4
range	63–144	63–144	82–130	80–146	84–138	80–146
WMS-III DMI
mean	112.6	116.7	108.3	111.8	113.5	110.1
standard deviation	17.3	20.4	12.5	16.0	14.9	17.2
range	52–140	52–140	86–132	83–150	83–140	84–150
Duration of Heavy Drinkingf (years)
mean	17.4	14.3	20.5	NA	NA	NA
standard deviation	7.7	5.2	8.5
range	5.0–35.0	6.0–25.0	5.0–35.0
Quantity Frequency Indexcde (ounces ethanol/day)
mean	11.2	8.7	13.7	0.3	0.2	0.4
standard deviation	8.8	5.8	10.5	0.6	0.5	0.7
range	2.7–38.4	2.7–28.1	4.5–38.4	0.0–2.6	0.0–2.4	0.0–2.6
Length of Sobrietycde (years)
Mean	8.3	10.6	5.9	2.1	3.6	0.5
standard deviation	10.3	11.1	8.8	6.4	8.5	1.3
range	0.1–32.3	0.1–32.1	0.1–32.3	0.002–29.2	0.002–29.2	0.002–5.1
Hamilton Rating Scale for Depressiong
mean	3.5	4.9	2.2	2.4	3.1	1.8
standard deviation	4.2	4.1	4.0	2.8	3.3	2.1
range	0–18	0–17	0–18	0–12	0–12	0–8

Participant Characteristics (p < 0.05)

aNCw > NCm

bNCm > AUDm

cAUD > NC

dAUDm > NCm

eAUDw > NCw

fAUDm > AUDw

gAUDw > AUDm

Abbreviations: WAIS—Wechsler Adult Intelligence Scale; WMS—Wechsler Memory Scale; IMI—Immediate Memory Index; DMI—Delayed Memory Index.

Selection procedures included an initial structured telephone interview to determine age, level of education, health history, and history of alcohol and drug use. It should be noted that we used the term “gender,” because we did not assess biological characteristics such as sex chromosomes or reproductive anatomy. Included participants were right-handed, had normal or corrected-to-normal vision, and spoke English as a first language. Participants were interviewed further to determine use of alcohol and other drugs, including prescription drugs that would affect the central nervous system. Current drug use excepting nicotine and caffeine was cause for exclusion. Criteria for exclusion also included history of liver disease, epilepsy, head trauma resulting in loss of consciousness for 15 minutes or more, HIV, symptoms of Korsakoff’s syndrome or dementia, and schizophrenia. Additionally, individuals who failed MRI screening (e.g., metal implants and obesity) were excluded.

Neuropsychological testing was conducted at the Department of Veterans Affairs (VA) Boston Healthcare System facility. Participants completed a medical history interview, vision test, handedness questionnaire [52], Hamilton Rating Scale for Depression [53] and the Diagnostic and Statistical Manual (DSM-IV) Diagnostic Interview Schedule [54]. Participants also were administered the Wechsler Adult Intelligence Scale (WAIS-III or WAIS-IV) and the Wechsler Memory Scale (WMS-III or WMS-IV) [55, 56].

The participants with AUD met DSM-IV criteria for alcohol abuse or dependence, and consumed 21 or more alcoholic drinks per week for a total of five or more years. The extent of alcohol use was assessed by calculating Quantity Frequency Index (QFI) scores [57]. QFI approximates the amount, type, and frequency of alcohol consumption either over the last six months (control participants), or over the six months preceding cessation of drinking (participants with AUD) to yield an estimate of ounces of ethanol per day; this is similar to the number of drinks consumed per day. The participants with AUD were abstinent for at least four weeks before the scan date. Control participants who had consumed 15 or more drinks per week for any length of time, including binge drinking, were excluded.

Functional imaging task

Participants were given a delayed match-to-sample memory task in an MRI scanner, whereby they were asked to encode two faces that had one of three emotional valences (positive, negative, or neutral), followed by one of three types of distractor cues (alcoholic beverage, nonalcoholic beverage, or a scrambled image) and a probe stimulus (an emotional face) for comparison (Fig 1 and S1 Fig). This task was chosen specifically because AUD has been associated with deficits in emotional functions [5], and with face memory in particular [51], but women were not included in prior research. The faces were intended to display happy (positive), neutral, and sad (negative) expressions. The faces were shown in grayscale and were taken from a set of faces used in a previous study [51]. Face stimuli were displayed simultaneously for three seconds, followed by an asterisk (*) for one second. Participants were asked to maintain these faces in memory while distractor stimuli were shown (for three seconds), followed by an asterisk (*) for one second, immediately followed by a probe face (shown for two seconds) to assess memory for face identity, and ending with a variable-length fixation stimulus (+++; for 2 to 22 seconds, average 10 seconds). It should be noted that the results pertaining to the fMRI encoding portion of the task were analyzed separately from the distractor and probe portions of the task; behavioral and fMRI findings based upon analyses of the distractor and probe portions comprise a separate research report. The participants responded to the probe face with their right index or middle fingers, and psychophysiological recordings were taken from the left hand [58]. The event-related design used nine six-minute runs with 18 trials per run, for a total of 162 trials. There were 54 trials for each emotional face valence. Counter-balancing and inter-trial intervals were calculated with optseq2 [59].

Fig 1

Task presented during functional neuroimaging.

Two faces were presented simultaneously for 3 seconds, followed by an asterisk for one second. Next, a distractor was presented for 3 seconds, followed by an asterisk for 1 second. The probe face immediately followed, during which the subjects had been trained to respond with a button press with either their index or middle finger to indicate whether the probe face matched the encoded face. Three crosses served as the fixation stimulus and inter-trial interval, which lasted from 2 to 22 seconds (mean 10 seconds). The faces in this figure have been blurred to mask the identities of the individuals, but the research participants saw the original unblurred photographs.

Image acquisition, processing, and statistical analyses

The MRI brain images were acquired by a 3T scanner and processed using Freesurfer (https://surfer.nmr.mgh.harvard.edu/), as summarized in this paragraph and presented in detail in the following paragraphs. Structural scans were acquired at 1 x 1 x 1.33 mm resolution, and fMRI scans were acquired with 3.125 x 3.125 x 5 mm resolution and a TR of 2530 ms. Cortical surfaces were reconstructed from structural scans. First-level and group-level smoothing were set to 5 mm and 8 mm, and the cluster threshold was set to p < 0.05 with a primary threshold of p < 0.001. Contrasts for the facial emotion conditions included: positive, negative, and neutral vs. fixation; and the three contrasts between the conditions. Intergroup comparisons were made among the subgroups (men, women, AUD, NC, AUDm, NCm, AUDw, and NCw, as well as Group by Gender interactions. We selected eight a priori regions of interest (ROI) because their separate locations had previously been established to be involved in emotion and face processing, and we investigated group differences for each region. BOLD responses were entered as dependent variables in a repeated-measures ANOVA, with between-group factors of Group and Gender, and the within-subjects factor of facial Emotion.

Imaging was conducted at the Massachusetts General Hospital, Charlestown, MA. Data were acquired on a 3 Tesla Siemens (Erlangen, Germany) MAGNETOM Trio Tim MRI scanner with a 12-channel head coil. Sagittal T1-weighted MP-RAGE scans (TR = 2530 ms, TE = 3.39 ms, flip angle = 7˚, FOV = 256 mm, slice thickness = 1.33 mm, slices = 128, matrix = 256 x 192) were collected for all subjects. Echo planar fMRI scans were acquired axially with 5 mm slice thickness and 3.125 x 3.125 mm in-plane resolution (64 x 64 matrix), allowing for whole brain coverage (32 interleaved slices, TR = 2 s, TE = 30 ms, flip angle = 90°). Within each six-minute run, 180 T2*-weighted volumes were obtained. Functional volumes were auto-aligned to the anterior/posterior commissure line to ensure a similar slice prescription was employed across participants. Prospective Acquisition Correction (3D-PACE) was applied during acquisition of the functional volumes to minimize the influence of participants’ body motion [60]. A laptop running Presentation version 11.2 software (NeuroBehavioral Systems, Albany, CA) was used for visual presentation of the experimental stimuli and collection of participants’ responses. Stimuli were back-projected onto a screen at the back of the scanner bore and viewed by participants through a mirror mounted to the head coil. Participants wore earplugs to attenuate scanner noise.

Cortical surfaces were reconstructed using Freesurfer version 4.5.0 (http://surfer.nmr.mgh.harvard.edu) to obtain segmentation labels [61, 62] along with white matter and exterior cortical surfaces [63]. These were visually inspected on each coronal slice for every subject, and manual interventions (e.g., white matter volume corrections) were made as needed. In order to delineate small regions distinguished by gyri and sulci, we used the Destrieux atlas [64] for segmentation and parcellation of anatomical ROI in the functional analyses.

Effects of Group, Gender, and Emotion on BOLD signal were evaluated using a whole-brain cluster analysis, as well as ROI analyses. Processing of the functional data was performed using FreeSurfer Functional Analysis Stream (FS-FAST) (http://surfer.nmr.mgh.harvard.edu/), SPSS Version 17.0 (IBM, Chicago, IL, USA), and custom Matlab scripts (The MathWorks, Natick, MA).

Preprocessing of functional images for first-level (individual subject) analyses included motion correction, intensity normalization, and spatial smoothing with a 5-mm Gaussian convolution kernel at full-width half-maximum, as specified with FS-FAST. BOLD response was estimated using a Finite Impulse Response (FIR) model, which allows for estimation of the time course of activity (percent signal change for a given condition) within a voxel, vertex, or ROI for the entire trial period. For each condition, estimates of signal intensity were calculated for 2 pre-trial and 10 post-trial onset time points (TRs), for a total analysis window of 24 seconds. Motion correction parameters calculated during alignment of the functional images were entered into the analysis as external regressors. Alignment of the T2*-weighted functional images with T1-weighted structural volumes was accomplished through an automated boundary-based registration procedure [65]. These automated alignments were manually inspected to ensure accuracy.

For contrasts between experimental conditions, statistical maps were generated via FS-FAST for each research participant. Contrasts for the facial emotion conditions included: (1) positive faces vs. fixation, (2) negative faces vs. fixation, (3) neutral faces vs. fixation, (4) positive faces vs. negative faces, (5) positive faces vs. neutral faces, and (6) negative faces vs. neutral faces. Analyses of each of these contrasts included removal of prestimulus differences between the contrasted conditions by averaging the first three time points (two pre-trial and one post-trial onset) for each condition and subtracting this mean from each time point for that condition. Time points summed for inclusion in each contrast were chosen to reflect peak stimulus-related activity: FIR estimates of hemodynamic responses to the emotion effects were examined during the time period of 2–10 seconds post emotional face onset.

Second-level (group) analyses on cortical regions were accomplished using a surface-based morphing procedure for intersubject alignment and statistics [66], as performed with FS-FAST. Group-averaged signal intensities during each experimental condition relative to fixation were calculated using the general linear model in spherical space for cortical regions, and were mapped onto the canonical cortical surface fsaverage, generating group-level weighted random-effects t-statistic maps. The same procedure was performed for the volume with a subcortical mask (MNI305 space; 2mm isotropic voxels). An 8 mm full-width half-maximum smoothing kernel was employed for all group and intergroup maps.

Intergroup comparison t-statistic maps were generated to examine between-group effects by contrasting: (1) AUD vs. NC, (2) AUDm vs. NCm, (3) AUDw vs. NCw, (4) AUDm vs. AUDw, (5) NCm vs. NCw, and (6) men vs. women. Additionally, Group by Gender interaction maps for each contrast were calculated.

We selected multiple correction procedures and thresholds that balance Type I and Type II error levels. For the statistical maps, cluster-level corrections for multiple comparisons were applied using the permutation procedure implemented by the mri_glmfit-sim procedure included in FS-FAST 6.0 [67], with 1,000 permutations. We chose to use permutation testing to identify clusters, because it provides the best control for Type I error, as compared to correction procedures that incorporate Gaussian Random Field theory [67, 68]. A primary (vertex- and voxel-wise) threshold of p < 0.001 was applied, and clusters with p-values < 0.05 (further corrected for analysis of three spaces, left, right, and volume) were selected. A primary threshold of p < 0.001 is stringent, and therefore eliminates smaller, more regionally specific clusters, inflating Type II error. However, to test our specific hypotheses, we conducted separate group comparisons between the four subgroups, in addition to examining the Group by Gender interaction; when viewed as a single family, these additional comparisons inflate Type I error. As described in the Limitations, the direct assessment of group differences has an advantage over using a factorial ANOVA in that it could identify clusters in areas without the interaction.

The cluster regions were identified by the location of each cluster’s peak vertex or voxel according to the Destrieux atlas [64], but the clusters reported can be understood to span multiple functional regions [68]. That is, they are not limited to a single region, as reported by the maximal vertex or voxel.

Eight anatomically-defined ROI were selected for the emotional faces BOLD analyses to include regions identified a priori that are known to be involved in the recognition of emotions, and in visual processing and memory encoding of human faces. These were the amygdala, fusiform gyrus, hippocampus, parahippocampal gyrus, intraparietal sulcus, orbitofrontal cortex, superior temporal gyrus, and superior temporal sulcus. Left and right hemisphere regions were analyzed separately.

Statistical preprocessing and time course visualization of ROI data were performed using custom scripts written for Matlab version 7.4.0. Signal intensity for each region was averaged across all vertices (or voxels) included in the region for each condition on the individual participant level. To compute percent signal change for each participant within an ROI, signal estimate per condition and time point was divided by the average baseline activity for that participant in the same manner as for the statistical maps. Group and Group-by-Gender averages of the normalized time courses were computed for each condition, and were visualized by plotting the percent signal change for each condition at each time point (TR) of the trial.

For the ROI analyses, percent signal changes of the BOLD signal within each ROI were entered as dependent variables into repeated-measures ANOVA models with between-group factors of Group (alcoholic or control) and Gender (men or women) and within-subjects factor of facial Emotion (positive, negative, or neutral).

Results

Participant characteristics

Table 1 summarizes means, standard deviations, and ranges of participant demographics, IQ and memory test scores, and drinking variables. The AUD and NC groups did not differ significantly by age (mean age 54 years), and although NCw were older than NCm, control groups did not differ significantly from the respective AUD groups of the same gender. AUDm had one year less education than NCm. Groups did not differ significantly on WAIS-III Full Scale IQ scores. While AUDw had higher Hamilton Rating Scale for Depression scores than AUDm, the average scores for all four subgroups (AUDm, AUDw, NCm, and NCw) were low (all means below 5, whereas mild depression threshold is 8), suggesting that depression contributed little to our observed gender differences. The AUD participants drank 11 drinks per day on average, had a mean duration of 17 years of heavy drinking, and were sober for an average of eight years. The differences between AUDm and AUDw for QFI and length of sobriety were not significant, but the DHD for the AUDm was significantly longer by 6.2 years compared to the AUDw. Five NCm and two NCw reported being lifetime abstainers, and one NCm reported three years of binge drinking in college, and sporadic drinking thereafter.

Neuroimaging cluster analyses

Group-level cluster analyses of each facial emotion condition vs. fixation yielded clusters too large to be described in an anatomically-relevant way with a single peak location. Therefore, these data are summarized qualitatively in the text, and they are illustrated in Fig 2. Group-level clusters are reported in S1 Table for contrasts with fixation (Positive vs. Fixation, Negative vs. Fixation, Neutral vs. Fixation) and in Table 2 for emotion contrasts (positive vs. negative, positive vs. neutral, and negative vs. neutral). For voxel-wise analyses of contrasts with fixation, no clusters were significant. Intergroup clusters are reported for each emotion condition vs. fixation (Table 3), and no intergroup clusters for emotion contrasts were significant.

Fig 2

Cortical surface cluster maps: Positive vs. fixation, lateral.

The left three columns show group maps, and the right three columns show group comparisons. The top two rows represent the left hemisphere, and the bottom two rows represent the right hemisphere. The clusters in this figure had a vertex wise threshold of p < .001 with a minimum cluster size of 100 mm2. This can result in more clusters being visible than are listed in Table 3 and S1 Table, wherein numbers were derived using permutation testing (cluster threshold p < .05, further corrected for analyses of left, right, and volume spaces).

Table 2

Emotion whole brain group cluster analysis: Positive vs. negative, positive vs. neutral, negative vs. neutral.

Negative Faces vs. Positive Faces
Structure at Peak Vertex	Size (mm2)	MNIX	MNIY	MNIZ	CWP	Group	Contrast Direction
Right Superior Temporal Sulcus	780.0	48.4	-45.3	6.2	0.003	NC	NEG > POS
Right Anterior Occipital Sulcus	409.0	40.1	-68.1	0.7	0.006	NC	NEG > POS
Right Precentral Gyrus	392.7	45.5	1.7	45.7	0.006	NC	NEG > POS
Right Occipital Pole	172.2	21.3	-89.5	-6.4	0.015	NC	NEG > POS
Right Inferior Frontal Gyrus Pars Triangularis	129.7	52.3	29.8	5.8	0.027	NC	NEG > POS
Right Superior Temporal Sulcus	469.9	48.4	-41.4	-2.5	0.009	WOMEN	NEG > POS
Right Occipital Pole	156.6	19.5	-90.2	-6.5	0.036	WOMEN	NEG > POS
Left Middle Anterior Cingulate	135.7	-11.9	19.3	30.1	0.018	AUDm	NEG > POS
Right Superior Temporal Sulcus	363.1	46.8	-45.1	7.8	0.003	NCm	NEG > POS
Right Inferior Frontal Sulcus	312.3	36.5	19.0	25.9	0.003	MEN	NEG > POS
Neutral Faces vs. Positive Faces
Structure at Peak Vertex	Size (mm2)	MNIX	MNIY	MNIZ	CWP	Group	Contrast Direction
Left Superior Frontal Gyrus	156.2	-8.9	0.8	55.3	0.012	AUD	NEU > POS
Left Middle Frontal Gyrus	222.4	-39.1	38.4	27.3	0.003	NC	NEU > POS
Right Superior Frontal Gyrus	307.6	6.8	1.2	65.6	0.006	NC	NEU > POS
Right Middle Frontal Sulcus	142.1	24.7	44.1	27.8	0.033	NC	NEU > POS
Left Fronto-marginal Gyrus (of Wernicke) and Sulcus	88.82	-24.2	51.9	-5.3	0.048	NC	NEU > POS
Right Superior Frontal Gyrus	85.09	16.4	-2.4	67.0	0.027	NCw	NEU > POS
Left Mid-Anterior Cingulate	217.8	-10.7	12.9	49.9	0.006	WOMEN	NEU > POS
Right Superior Frontal Gyrus	145.7	15.4	-2.5	67.6	0.015	WOMEN	NEU > POS
Left Superior Frontal Gyrus	140.2	-10.1	3.7	67.1	0.021	WOMEN	NEU > POS
Right Superior Temporal Sulcus	388.5	48.3	-47.8	22.0	0.003	NCm	NEU > POS
Right Middle Frontal Gyrus	222.1	32.9	46.7	16.7	0.009	NCm	NEU > POS
Right Occipital Pole	149.6	11.8	-88.0	0.7	0.021	NCm	NEU > POS
Structure at Peak Voxel	Size (mm3)
Right Putamen	3472	18.0	13.0	-5.0	0.003	NC	NEU > POS
Negative Faces vs. Neutral Faces
Structure at Peak Vertex	Size (mm2)	MNIX	MNIY	MNIZ	CWP	Group	Contrast Direction
Right Anterior Transverse Collateral Sulcus	230.2	38.6	-19.3	-27.0	0.015	AUD	NEG > NEU
Right Middle Temporal Gyrus	161.9	56.1	-57.7	1.6	0.018	AUD	NEG > NEU

Coordinates are presented for peak vertices and voxels within significant clusters of activation. Minimum significance for all vertices (or voxels) within a cluster were p = 0.001. Clusters were selected using permutation testing at p < 0.05. For context, S1 Table presents significant clusters for contrasts with fixation (Positive vs. Fixation, Negative vs. Fixation, Neutral vs. Fixation). S6 through S9 Figs show the corresponding subcortical cluster maps, and S10 through S15 Figs show the corresponding cortical cluster maps. The clusters reported can be understood to span multiple functional regions [68]. That is, they are not limited to a single region, as reported by the maximal vertex or voxel. Abbreviations: MNIX, MNIY, MNIZ—Montreal Neurological Institute 305 Atlas coordinates of maximum vertex; CWP—Cluster-wise p value.

Table 3

Emotion whole brain intergroup cluster analysis: Positive vs. fixation, negative vs. fixation, neutral vs. fixation, positive vs. negative, positive vs. neutral, negative vs. neutral.

Positive Faces vs. Fixation
Structure at Peak Vertex	Size (mm2)	MNIX	MNIY	MNIZ	CWP	Group Comparison	Condition Contrast
Left Superior Temporal Gyrus	494.7	-48.4	-51.2	44.2	0.003	NCw > AUDw	FIX > POS
Left Supramarginal Gyrus	483.2	-50.3	-50.8	44.1	0.006	NCw > NCm	POS > FIX
Right Intraparietal Sulcus	265.0	26.6	-54.3	52.6	0.036	NCw > NCm	POS > FIX
Left Angular Gyrus	644.8	-48.4	-51.2	44.2	0.003	WOMEN > MEN	POS > FIX
Left Superior Temporal Gyrus	323.6	-50.4	9.5	-17.6	0.024	Group by Gender:	FIX > POS
						NCw > AUDw;
						AUDm ≈ NCm
Negative Faces vs. Fixation
Structure at Peak Vertex	Size (mm2)	MNIX	MNIY	MNIZ	CWP	Group Comparison	Condition Contrast
Left Superior Frontal Gyrus	267.9	-6.6	33.8	49.8	0.042	AUDm > NCm	NEG > FIX
Left Supramarginal Gyrus	447.7	-50.3	-50.8	44.1	0.015	NCw > NCm	NEG > FIX
Left Angular Gyrus	378.5	-48.4	-51.2	44.2	0.018	MEN > WOMEN	FIX > NEG
Left Superior Frontal Gyrus	319.5	-7.0	26.9	40.8	0.030	Group by Gender:	NEG > FIX
						AUDm > NCm;
						AUDw ≈ NCw
Neutral Faces vs. Fixation
Structure at Peak Vertex	Size (mm2)	MNIX	MNIY	MNIZ	CWP	Group Comparison	Condition Contrast
Right Anterior Cingulate	285.9	11.5	52.8	4.1	0.027	NCw > NCm	FIX > NEU
Left Supramarginal Gyrus	307.3	-48.9	-50.4	44.3	0.027	WOMEN > MEN	NEU > FIX

Coordinates are presented for peak voxels within significant clusters of activation. Minimum between-group significance for all vertices (or voxels) within a cluster was p = 0.001. Clusters were selected using permutation testing at p < 0.05. No clusters were significant for contrasts between facial emotions (Positive vs. Negative, Positive vs. Neutral, Negative vs. Neutral). Fig 2 and S1 through S5 Figs show the corresponding cortical cluster maps. As noted in the text, the colors and corresponding directions of effects in the figures are shown by calculating results for faces minus fixation, whereas in Table 3, the directions of absolute values are presented. The clusters reported can be understood to span multiple functional regions [68]. That is, they are not limited to a single region, as reported by the maximal vertex or voxel. Abbreviations: MNIX, MNIY, MNIZ—Montreal Neurological Institute 305 Atlas coordinates of maximum vertex; CWP—Cluster-wise p-value.

The AUD and NC groups of both genders utilized a distributed network of cortical brain regions to process faces of all three emotional valences as compared to the fixation stimulus. As an example, Fig 2 shows the t-statistic cluster map of the contrast of positive vs. fixation displayed on the lateral surface; the medial and lateral views of the negative vs. fixation and neutral vs. fixation are shown in S2 through S5 Figs. Subcortical volume-based t-statistic cluster maps are shown in S6 through S9 Figs. Several face-activated regions were more responsive when participants viewed the face stimuli than during the fixation condition: dorsolateral prefrontal cortex, motor cortex, anterior insula, inferior temporal cortex (including fusiform), parietal cortex, the occipital lobes, and limbic structures. A different set of fixation-activated regions was more active during fixation than during the face conditions, including regions that are typically more active during rest than during attentionally-demanding cognitive tasks [69].

As was observed for the contrast of faces vs. fixation, results for the contrasts between emotions revealed several regions, but the clusters were of smaller spatial extent and less consistent across participant groups. Significant clusters are summarized in Table 2 and shown in S10 through S15 Figs. Positive faces elicited the least activation, as compared to both neutral and negative faces. These effects were significant primarily in regions of the frontal lobes, although additional regions identified were in the occipital and temporal lobes.

Clusters of significant between-group differences on each emotion condition vs. fixation are described in Table 3. The results were complicated and differed by brain region, contrast direction, and group comparison. For positive faces, significant clusters were identified within all lobes of the brain. The group contrast directions for all of these clusters indicated greater activation values in the subgroup of NCw compared to the AUDw and NCm, and the effects were observed across both fixation-activated and face-activated regions. It should be noted that the colors and corresponding directions of effects in the figures are shown by calculating results for faces minus fixation, whereas in Table 3, the directions of absolute values are presented. For example, in the left superior temporal gyrus, Table 3 shows the NCw had greater fixation activation than AUDw, and in Fig 2, the NCw are shown to have lower face activation than AUDw (although it may be awkward to think of “negative face activation”).

Significant Group by Gender interaction effects for temporal and frontal regions were driven by the lower activation of NCm than NCw, while AUDm had similar or greater activation than AUDw. The negative faces revealed a pattern of group differences that encompassed many brain regions. For supramarginal regions, NCw had greater values than NCm. As was found for the positive faces, a frontal cluster was identified where a significant Group by Gender interaction was driven by stronger negative vs. fixation contrast values obtained from AUDm than NCm, and a smaller difference was observed between AUDw and NCw. The neutral faces revealed two clusters, with NCw having greater contrasts than NCm in the fixation-activated anterior cingulate cortex (S5 Fig). No clusters were identified with significant group differences for contrasts between emotional face conditions (Positive vs. Negative, Positive vs. Neutral, Negative vs. Neutral).

Neuroimaging region of interest analyses

Results of repeated-measures ANOVAs examining between-subjects effects of Group and Gender and within-subjects effects of facial Emotion on BOLD percent signal change within each ROI are reported below. Means and standard deviations represent the percent signal change across each ROI for the time period of 2 to 10 seconds post-face stimulus onset.

Intraparietal sulcus

A significant main effect of Group was found for left intraparietal sulcus activation during encoding of the emotional faces (F = 4.172, p = 0.044). As can be seen in Fig 3, activation to the faces was substantially higher than to the fixation for the AUD and NC groups alike. The NC group had more activity (vs. fixation) in this region during face encoding (Mean = 0.27, SD = 0.12) than did the AUD group (Mean = 0.21, SD = 0.13). Activity in the right intraparietal sulcus did not vary significantly by Group, Gender, or facial Emotion.

Fig 3

Left intraparietal sulcus.

Error bars represent the standard error of the mean. The analysis window used to examine the encoded faces was 2 to 10 seconds. The first peak (at approximately 7 seconds) represents brain activity resulting from the encoded faces. The activity following that peak (longer than 10 seconds) shows responses to the distractor and probe stimuli, to be described in a future research report.

Hippocampus

Overall, hippocampal activation in response to the encoded faces (vs. fixation) was negligible for all groups (Fig 4). Although there was a significant Emotion by Group by Gender interaction for the left hippocampus (F = 4.005, p = 0.049), intergroup comparisons were not significant. The directions of the effects were as follows: For NCm, activation was higher for positive faces vs. fixation (Mean = 0.04, SD = 0.09) than for neutral faces vs. fixation (Mean = 0.01, SD = 0.10) and for negative faces vs. fixation (Mean = 0.02, SD = 0.07); the same means were observed for AUDw. This pattern appeared stronger than that observed for the other subgroups (ALCm Means: positive = -0.01, neutral = -0.01, negative = -0.01; NCw Means: positive = -0.02, neutral = 0.01, negative = 0.01). Activity in the right hippocampus did not vary significantly by Group, Gender, or Emotion.

Fig 4

Left hippocampus.

Error bars represent standard error of the mean. The percent signal change represents brain activity resulting from presentation of the task stimuli. The analysis window used to examine the encoded faces was 2 to 10 seconds. Brain activity following that period shows responses to the distractor and probe stimuli, to be described in a future research report.

Amygdala, fusiform, orbitofrontal cortex, parahippocampal cortex, superior temporal gyrus, and superior temporal sulcus

Activity in these regions did not vary significantly by Group, Gender, or Emotion.

In summary, for ROI analyses, we observed higher responsivity during face encoding in the NC group compared to the AUD group, in the left intraparietal sulcus, a region that has been identified as playing an important role in focusing attention to enhance working memory [70]. In the left hippocampus, a region involved in memory, a significant interaction of Emotion, Group, and Gender, indicated that the NCm activated this region more to positive faces than to neutral faces, as compared to the other subgroups. However, the magnitudes of these effects were small.

Discussion

In this study, we used an fMRI task to explore how gender and AUD interacted to influence brain activation levels during emotional processing and memory. Here, we discuss the findings that were similar for the AUD and NC groups of both genders, that is, greater cortical responsivity to negative and neutral facial expressions than to positive faces in frontal, temporal, and occipital regions. We also discuss the finding that the AUD cohort evidenced greater fusiform activation in response to negative than to positive facial expressions. Next, as a background with which to frame our results regarding gender differences, we discuss the activation clusters identified from comparisons between AUD and NC groups. We then highlight the clusters found with Group by Gender interaction effects, and the implications of the lower activation levels in NCm compared to NCw in comparison to those between AUDm and AUDw. Finally, we note that results for two of the eight ROI we examined had significant group comparisons: The left intraparietal sulcus showed lower activation by the AUD group than the NC group, and the left hippocampus showed a complex Group by Gender by Emotion interaction.

Responsivity to facial expressions

The whole-brain group-level cluster analyses comparing activation among facial emotion conditions showed that participants had higher responses both to negative and to neutral faces than to positive faces in frontal cortex, superior temporal sulcus, and occipital pole. While it is unclear why neutral faces would elicit higher activation than positive faces, a meta-analysis [14] showed that negative faces were widely reported to evoke strong activation. Consistent with our results, the frontal cortex is involved in recognition of facial emotions [51]. Further, enhanced activity during emotional face recognition in response to disgusted, angry, or fearful expressions relative to neutral expressions, has been reported in inferior frontal cortex [71]. In addition to the involvement of frontal regions in facial processing, the superior temporal sulcus has been implicated in differentiating emotional facial expressions in nonclinical populations [18, 19, 72]. This pattern of face activation was consistent with the AUD group’s responses to negative faces, which were greater than to neutral faces in the collateral sulcus (adjacent to the anterior fusiform gyrus). While the fusiform is more commonly implicated in processing facial identity information [18], some studies also have implicated the role of the collateral sulcus [19, 72]. Interestingly, a review of studies that focused on valence effects of face processing [73] reported a developmental progression of biases that favor “more efficient processing of positive over negative faces,” wherein children more easily process positive faces, but with age, this evolves into a more efficient processing of negative facial expressions.

Intergroup clusters

Examination of whole-brain intergroup cluster comparisons revealed gender differences among controls wherein the difference in responses to emotional faces vs. fixation was larger for NCw than NCm in the supramarginal gyrus and intraparietal sulcus, adjacent areas that may have important roles in emotional processing [74]. A medial portion of the superior frontal gyrus was identified wherein AUDm had more activation to negative faces as compared to fixation than did NCm. However, this cluster should be interpreted in the context of a significant interaction between group and gender for the same region.

Interactions between group and gender

The NC participants demonstrated more reliable gender differences in neural responses than did the AUD group, and widespread neural responses to these stimuli were more pronounced in NCw than in NCm. By comparison, gender differences in the AUD group were muted. The Group by Gender interaction effects indicated stronger responses to emotional faces by AUDm than NCm in the left superior frontal gyrus, a significantly more pronounced group difference than the one observed for women (S3 Fig). In an anterior portion of the left superior temporal gyrus (extending to the middle temporal gyrus) that was more highly activated by fixation than positive faces, we observed a similar interaction effect wherein NCw had greater fixation-related activation than AUDw, a group difference that was more pronounced than the one observed for men (Fig 2).

We had hypothesized that (1) AUD-related abnormalities would differ for men and women; (2) AUDw would show hyperactivation to emotional faces; and (3) AUDm would show hypoactivation compared to NCm. Although we did observe gender differences in AUD-related abnormalities, the other hypotheses were not confirmed, namely we found that activation by AUDw to emotional faces was hypoactive, and activation by the AUDm was hyperactive. Possible explanations for these findings derive from neuropsychiatric literature. For example, women are more likely than men to be diagnosed with depression, anxiety disorders, and eating disorders [75]. Therefore, alcohol consumption by AUDw might reflect a predisposition to abnormal regional activation in either direction (hyper- or hypo-responsivity) that leads to depression, anxiety disorders, and eating disorders. In contrast, AUDm might consume alcohol to enhance pleasurable activities [76], although gender differences in drinking motives are not always observed [47]. Whether cause or effect, AUD in men and women could become part of a vicious cycle that serves as a coping mechanism and also reinforces abnormalities in brain responsivity.

Regions of interest

The ROI analyses examining effects of Group, Gender, and Emotional face valence yielded few significant results. Overall, in the left intraparietal sulcus, a region that has been singularly identified as playing an important role in focusing attention to enhance working memory [70], we found high levels of activation to the faces in both the NC and AUD groups. Moreover, the responses were greater for the NC group than for the AUD group, thereby supporting other evidence of working memory deficits in AUD populations [5]. We also found that the AUD group showed hypoactivation of the left intraparietal sulcus when encoding the identity of the emotional face stimuli. Of interest, Majerus et al. [77] reported that during the performance of a face encoding task by healthy young adults, activation of the left intraparietal sulcus showed preferential functional connectivity with right temporal, inferior parietal, and medial frontal areas involved in detailed face processing. The authors noted that these results supported an attentional account of left intraparietal sulcus involvement in visual short term memory, and highlighted the importance of the left intraparietal sulcus as an attentional modulator in a variety of short term memory tasks. The findings of Majerus and colleagues are consistent with our interpretation that the AUD group’s hypoactivation in the intraparietal sulcus reflects memory and attentional impairments in association with chronic abuse of alcohol [3-6]. The significant interaction of Emotion, Group, and Gender we found in the left hippocampus, showing that NCm and AUDw activated this region more for positive faces than for neutral faces, was not observed for NCw and AUDm. However, as compared to fixation, activation of the hippocampus to the encoded faces was negligible in all groups, which constrains the interpretability of these results.

Limitations

As noted in the Methods, the encoded face fMRI data were acquired in the context of a task that measured the influence of distractor cues. The task contained a memory component measuring participants’ response accuracies and reaction times to probe faces (not to encoded faces). Therefore, interference from the distractor images may have influenced brain-activation or behavior. However, the distractor stimuli were equally distributed following each of the three types of emotional face valence conditions in order to ameliorate any differential impact of the distractors. Results obtained from the influences of the distractor and probe stimuli on emotional processing will be presented in a future report.

While we performed multiple comparisons correction procedures by using permutation testing, we conducted ROI analyses without correcting for multiple comparisons. Each ROI was selected individually, because each of them was independently derived from results of previous literature. However, this approach also could be considered in the context of a family of comparisons with an elevated false positive error rate.

For our ROI results, we employed a traditional approach to statistical analyses, by examining interaction effects, followed by determining the significance of group differences. Instead of using a factorial ANOVA to assess the significant activation clusters with subsequent within-cluster post-hocs (that is, combining group comparisons with interaction effects for vertex-wise and voxel-wise analyses), we assessed the activation clusters for the group differences directly across all vertices and voxels. Even though this approach engenders additional comparisons and increases the likelihood of making Type I errors, it has the benefit of identifying clusters in locations other than those where interactions are present. We used a primary (vertex- or voxel-wise) threshold of p < 0.001 for each group comparison. By considering these analyses to be a family of seven comparisons, the Bonferroni-adjusted family-wise primary threshold would be p < 0.007.

Because this study employed cross-sectional observations, we cannot determine whether hazardous drinking caused, or resulted from, dysregulated emotional reactivity. Further, because we had limited information about the potentially confounding variable of smoking status, we did not include it in our analyses. The effects of abstinence from smoking have been associated with increased emotional reactivity in response to negative stimuli [78] and research additionally has implicated interactions between smoking and alcoholism [79, 80], so smoking effects could have influenced our results.

Finally, our AUD subjects were abstinent for a minimum of four weeks but an average of 8.3 years. This wide range of sobriety speaks to the persistent nature of emotion processing deficits in AUD populations, and whether such deficits recover differently over the course of short- and long-term abstinence in men and women [13]. Moreover, we believe that the alcohol consumption and abstinence characteristics of our AUD cohort are representative of the national population [36], thereby improving generalizability of our results. Additionally, the topic of persistence vs. recovery remains a promising direction for future studies, for example, with analyses of brain activity in relation to length of sobriety and gender for the AUD group [81, 82]. The average length of sobriety was longer for AUDm than for AUDw, which might have influenced the gender differences we observed. Since there were no abstinence values for the NC group, the variable could not be used as a covariate in an analysis of group differences.

Cortical surface cluster maps: Positive vs. fixation, medial.

(PNG)

Click here for additional data file.

Cortical surface cluster maps: Negative vs. fixation, lateral.

(PNG)

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Cortical surface cluster maps: Negative vs. fixation, medial.

(PNG)

Click here for additional data file.

Cortical surface cluster maps: Neutral vs. fixation, lateral.

(PNG)

Click here for additional data file.

Cortical surface cluster maps: Neutral vs. fixation, medial.

(PNG)

Click here for additional data file.

Subcortical volume cluster maps: AUDw, positive faces vs. fixation.

Cluster-corrected at p < .001 with minimum cluster size 300 mm3. Shown in neurological convention (left brain on the left side of the image).

(PNG)

Click here for additional data file.

Subcortical volume cluster maps: AUDm, positive faces vs. fixation.

Cluster-corrected at p < .001 with minimum cluster size 300 mm3. Shown in neurological convention (left brain on the left side of the image).

(PNG)

Click here for additional data file.

Subcortical volume cluster maps: NCw, positive faces vs. fixation.

Cluster-corrected at p < .001 with minimum cluster size 300 mm3. Shown in neurological convention (left brain on the left side of the image).

(PNG)

Click here for additional data file.

Subcortical volume cluster maps: NCm, positive faces vs. fixation.

Cluster-corrected at p < .001 with minimum cluster size 300 mm3. Shown in neurological convention (left brain on the left side of the image).

(PNG)

Click here for additional data file.

Cortical surface cluster maps: Positive vs. negative, lateral.

The left three columns show group maps, and the right three columns show group comparisons. The top two rows represent the left hemisphere, and the bottom two rows represent the right hemisphere. The clusters in this figure had a vertex wise threshold of p < .001 with a minimum cluster size of 100 mm2. This can result in more clusters being visible than are listed in Table 2, wherein numbers were derived using permutation testing (cluster threshold p < .05, further corrected for analyses of left, right, and volume spaces).

(PNG)

Click here for additional data file.

Cortical surface cluster maps: Positive vs. negative, medial.

The left three columns show group maps, and the right three columns show group comparisons. The top two rows represent the left hemisphere, and the bottom two rows represent the right hemisphere. The clusters in this figure had a vertex wise threshold of p < .001 with a minimum cluster size of 100 mm2. This can result in more clusters being visible than are listed in Table 2, wherein numbers were derived using permutation testing (cluster threshold p < .05, further corrected for analyses of left, right, and volume spaces).

(PNG)

Click here for additional data file.

Cortical surface cluster maps: Positive vs. neutral, lateral.

The left three columns show group maps, and the right three columns show group comparisons. The top two rows represent the left hemisphere, and the bottom two rows represent the right hemisphere. The clusters in this figure had a vertex wise threshold of p < .001 with a minimum cluster size of 100 mm2. This can result in more clusters being visible than are listed in Table 2, wherein numbers were derived using permutation testing (cluster threshold p < .05, further corrected for analyses of left, right, and volume spaces).

(PNG)

Click here for additional data file.

Cortical surface cluster maps: Positive vs. neutral, medial.

The left three columns show group maps, and the right three columns show group comparisons. The top two rows represent the left hemisphere, and the bottom two rows represent the right hemisphere. The clusters in this figure had a vertex wise threshold of p < .001 with a minimum cluster size of 100 mm2. This can result in more clusters being visible than are listed in Table 2, wherein numbers were derived using permutation testing (cluster threshold p < .05, further corrected for analyses of left, right, and volume spaces).

(PNG)

Click here for additional data file.

Cortical surface cluster maps: Negative vs. neutral, lateral.

The left three columns show group maps, and the right three columns show group comparisons. The top two rows represent the left hemisphere, and the bottom two rows represent the right hemisphere. The clusters in this figure had a vertex wise threshold of p < .001 with a minimum cluster size of 100 mm2. This can result in more clusters being visible than are listed in Table 2, wherein numbers were derived using permutation testing (cluster threshold p < .05, further corrected for analyses of left, right, and volume spaces).

(PNG)

Click here for additional data file.

Cortical surface cluster maps: Negative vs. neutral, medial.

The left three columns show group maps, and the right three columns show group comparisons. The top two rows represent the left hemisphere, and the bottom two rows represent the right hemisphere. The clusters in this figure had a vertex wise threshold of p < .001 with a minimum cluster size of 100 mm2. This can result in more clusters being visible than are listed in Table 2, wherein numbers were derived using permutation testing (cluster threshold p < .05, further corrected for analyses of left, right, and volume spaces).

(PNG)

Click here for additional data file.

Emotion whole brain group vertex cluster analysis: Positive vs. fixation, negative vs. fixation, neutral vs. fixation.

Coordinates are presented for peak vertices within significant clusters of activation for surface analyses of the left and right hemispheres. Minimum significance for all vertices within a cluster were p = 0.001. Clusters were selected using permutation testing at p < 0.05. For context, Table 2 presents significant intergroup clusters for these contrasts; Fig 2 and S1 through S5 Figs show the corresponding cortical cluster maps. The clusters reported can be understood to span multiple functional regions [68]. That is, they are not limited to a single region, as reported by the maximal vertex. Abbreviations: ClusterNo—The cluster number within the analysis; Max—Maximum -log(p value) within the cluster; VtxMax—Vertex number for peak vertex; MNIX, MNIY, MNIZ—Montreal Neurological Institute 305 Atlas coordinates of maximum vertex; CWP—Cluster-wise p value; CWPLow, CWPHi—90% confidence interval for the CWP; NVtxs—number of vertices in cluster; Annot—Destrieux annotation for peak vertex.

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Click here for additional data file.

(CSV)

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(CSV)

Click here for additional data file.

19 Feb 2020

PONE-D-20-00671

Brain responsivity to emotional faces differs in men and women with and without a history of alcohol use disorder

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Reviewer #1: This study replicates a previous fMRI study but includes women in order to begin to better understand sexual dimorphism in the brain with respect to the effects of alcoholism on emotional processing and memory. A total of 42 individuals with alcohol use disorder (AUD) and 42 healthy controls were included: half of each group (n=21) was women. All participants completed a delayed match to sample emotional face memory fMRI task. Regions activated by the task were similar to those previously identified in the literature, especially for normal controls. Within both groups, sex differences emerged. In the healthy controls, sex differences were more pronounced and neural responses were more widespread in women than men. Sex differences in the AUD group were attenuated and sometimes in the opposite direction than those observed in healthy controls.

The groups were well matched and the activation patterns were generally similar across the groups, lending assurance to replicability. The writing was clear and succinct.

Some things to address:

AUD subjects were abstinent for a minimum of 4 weeks but an average of 8 years. This wide range of sobriety supports persisting deficits in emotional and memory processing, but also in a heterogenous group, could length of sobriety have contributed to sex differences. Can length of sobriety be used as a co-variate in the analysis?

Why was the Destrieux atlas used? Can some sort of justification be presented?

AUDw had higher Hamilton Depression Scale (HDS) scores than AUDm. Can the sex differences be accounted for by depression? Can the HDS score be used as a covariate?

Positive faces elicited the least activation, as compared to both neutral and negative faces. Can the authors discuss this result in a bit more detail?

Reviewer #2: This manuscript examines emotional responsiveness to faces in adults with alcohol use disorder. This is an interesting question where further work is needed, as many studies to date have been underpowered to address this question. Also, more clearly investigating gender differences in AUD could help with more personalized assessment/treatment of AUD. However, there are several outstanding methodological and analytic questions that should be addressed before I recommend this manuscript for publication. I also believe that providing some clear hypotheses (with reasoning for these hypotheses) in the introduction would help with framing the overall manuscript and with data interpretation.

Introduction

Page 4:

In paragraph 2 on Page 4, the authors transition from talking about the neurobiology underlying facial identity versus expression recognition in the previous paragraph to talking about gender differences in AUD. As a reader, I found this transition to be somewhat confusing and would suggest reframing paragraph 2 to begin by discussing the neurobiology underlying face and emotion processing and then transitioning to the potential implications for gender differences.

Page 4-5:

The authors do not seem to offer any a priori hypotheses regarding gender differences in facial processing in AUD and only state that they wished to “characterize abnormalities in neural activation among abstinent participants with AUD.” I think that stating a clear hypothesis about what the authors expected to find would be helpful in terms of framing this study within the context of the current literature and guiding the subsequent analyses and discussion.

Methods

Page 8:

“The following a priori ROI were selected due to their previously established involvement in the emotion and face processing: amygdala, fusiform gyrus, hippocampus, parahippocampal gyrus, intraparietal sulcus, orbitofrontal cortex, superior temporal gyrus, and superior temporal sulcus.”

Did the authors correct for multiple comparisons across multiple a priori ROI’s? If not, could the authors clarify why they did not correct for multiple comparisons?

Page 8:

“For surface analysis, the cluster threshold was set to 100 mm2 contiguous voxels of p <

0.001 for each contrast; for volume analysis, the minimum volume of contiguous voxels was 300

mm3.”

How were these thresholds determined? Using AFNI clustsim or a similar tool? Also, what do 100mm2 and 300 mm3 translate to in terms of voxels?

Page 9:

“For each participant, contrasts for the facial emotion conditions included: (1) positive faces vs. fixation, (2) negative faces vs. fixation, (3) neutral faces vs. fixation, (4) positive faces vs. negative faces, (5) positive faces vs. neutral faces, and (6) negative faces vs. neutral faces. Intergroup comparisons were: (1) AUD vs. NC, (2) AUDm vs. NCm, (3) AUDw vs. NCw, (4) AUDm vs. AUDw, (5) NCm vs. NCw, (6) men vs. women, and (7) Group by Gender interactions.”

For the cluster-level analyses, can the authors explain why they chose to implement individual t-tests for each contrast/intergroup comparison? In my view it would be simpler to model this as a 2 (Group: AUD vs NC) x 2 (Gender: Male vs Female) x 3 (Facial Expression: Positive, Negative, Neutral) repeated measures ANOVA and then submit any significant F-tests to post-hoc testing.

I would also recommend moving the supplemental methods section and supplemental Figure 1 to the main text to give more details regarding the MRI data collection, analysis, and the behavioral paradigm.

Results

Page 9:

In the participants section, could the authors state whether there were any differences in alcohol quantity/frequency between males and females, particularly within the AUD group? I see that these differences are listed in Table 1, but it would be helpful to have these results in the main text.

Page 10:

“A different set of fixation-activated regions was more active during fixation than

during the face conditions, forming the network known as the default mode network, because

those regions typically are more active during rest than during attentionally-demanding cognitive tasks (Buckner et al., 2008). The regions making up this network include the superior frontal cortex, medial prefrontal cortex, medial temporal lobe structures, the middle temporal gyrus, the posterior cingulate cortex plus precuneus, and the angular gyrus.”

I would recommend removing this part regarding the default mode network as the main contrasts of interest primarily involve regions that are responsive to facial emotions and DMN findings are not discussed in the discussion section.

Page 11-12:

“The three clusters that were identified with significant group differences for contrasts between emotional face conditions had peak voxels contained within the left and right amygdala (872 mm3 and 384 mm3, respectively), and left hypothalamus (464 mm3).”

Could the authors clarify which clusters these are or display them in a figure? I do not see the any clusters listed as amygdala or hypothalamus in Table 3.

Page 12-13:

In the Neuroimaging Region of Interest Analyses section, did the intraparietal sulcus and/or the hippocampus show greater activation for faces relative to fixation within the ROI? If not, interpreting group differences may be difficult.

Finally- were there any group differences in behavior (either response times or accuracy) on the task? This would provide a bit more context in which to interpret the neuroimaging findings.

Discussion

Page 13-14:

The first paragraph of the discussion seems to discuss the main task findings; while this is important information, I think it would be helpful to have a short paragraph at the very beginning of the discussion summarizing the results in order to provide the reader a road map for the discussion.

Figures/Tables

It would be helpful to have a figure showing some of the cluster-level group differences that are listed out in Table 3. In particular, it would be helpful to have a figure that not only shows each group but also directly displays the cluster-level group differences, particularly within the amygdala/hypothalamus (as mentioned in the Results section on Pages 11-12).

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28 Aug 2020

Please see attached response to reviewers.

Submitted filename: PONE-S-20-00533-R1-reply.docx

Click here for additional data file.

6 Oct 2020

PONE-D-20-00671R1

Brain responsivity to emotional faces differs in men and women with and without a history of alcohol use disorder

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

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Reviewer #1: No

Reviewer #2: Yes

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Reviewer 1: AUD subjects were abstinent for a minimum of 4 weeks but an average of 8 years. This wide range of sobriety supports persisting deficits in emotional and memory processing, but also in a heterogenous group, could length of sobriety have contributed to sex differences. Can length of sobriety be used as a co-variate in the analysis?

Authors: We added text to the Limitations portion of the Discussion regarding sobriety with the following: “Regarding the variable of LOS, since there were no values for the NC group, the variable could not be used as a covariate in an analysis of group differences. However, we believe that the alcohol consumption and abstinence characteristics of our AUD cohort are representative of the national population (World Health Organization 2019), thereby improving generalizability of our results.”'

This variable (LOS) be used as a covariate among the AUD group only as a post hoc test to determine if it modulates connectivity.

Reviewer #2: The authors have largely responded to my reviews satisfactorily and I believe this paper makes an interesting contribution to the literature. However, I still have a couple of points of concern regarding the analysis.

i) In their response letter, the authors state that when they chose a fixed arbitrary cluster-size threshold, at least in part because “cluster sizes that have been set by using a cluster-wise p-value may lead to higher underlying false positive rates than those expected when determining cluster size thresholds using Gaussian random field theory (Eklund et al. 2016).”

The authors should consider using mri_glmfit-sim (or an equivalent program) to provide the cluster size threshold at an initial p-value of p=.001 that would be equivalent to a cluster corrected p<.05 or provide the equivalent p-value for a cluster corrected threshold with an initial p-value of p=.001 and a cluster size of 100 mm2. It is my understanding that the faulty assumptions in this procedure leading to the Eklund paper have since been fixed within most neuroimaging software packages (Greve and Fischl, 2017; Cox, Chen, Glen, Reynolds, and Taylor, 2017). Although I am sympathetic to the view that an alpha level of p<.05 is an arbitrary threshold and it should be carefully considered whether or not to actually use an alpha level of p<.05, readers should have some quantitative point of reference for judging the significance of the clusters reported.

ii) With regard to conducting F-tests for cluster-level analyses, the authors state in their response letter that “building an ANOVA model with cluster correction is not as simple as it is for an ROI analyses conducted with traditional statistical software... The cluster extents for the interaction F-tests and the clusters identified for the simple group comparison t-tests would be different, and we know of no neuroimaging software that provides a straightforward means of conducting vertex- and voxel-wise direct group comparisons within an interaction model, with respect to activation clusters.”

I would suggest conducting a whole-brain repeated measures 2 (Group: AUD vs NC) x 2 (Gender: Male vs Female) x 3 (Facial Expression: Positive, Negative, Neutral) ANOVA on the BOLD response data. Then, using each individual significant cluster for each interaction effect (Group-by-Gender, Group-by-Facial Expression, Gender-by-Facial Expression, Group-by-Gender-by-Facial Expression), extract the average parameter estimates for each within-subject condition (in this case, each of the three levels of facial expression) for each subject, and then import these data into SPSS (along with the data for the between-subjects variables) where post-hoc testing for interaction effects within a repeated measures model can be conducted.

I have been able to conduct this procedure successfully in SPM and AFNI in the past; is there not a parallel way to do this in Freesurfer? While this is not a perfect procedure, I believe that it does somewhat control for Type I error since it limits the amount of individual contrasts tested for in the main neuroimaging analysis and it allows for the directionality of the interaction to be probed.

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Reviewer #1: No

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[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

24 Feb 2021

Please see the attached response to the reviewers.

Submitted filename: PONE-S-20-00533-R2-reply.docx

Click here for additional data file.

8 Mar 2021

Brain responsivity to emotional faces differs in men and women with and without a history of alcohol use disorder

PONE-D-20-00671R2

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We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

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Academic Editor

PLOS ONE

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Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

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Reviewer #2: My concerns have been sufficiently addressed and I recommend publication of this manuscript without further revision.

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14 May 2021

PONE-D-20-00671R2

Brain Responsivity to Emotional Faces Differs in Men and Women with and without a History of Alcohol Use Disorder

Dear Dr. Sawyer:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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