Accurate identification of the language-dominant hemisphere is essential for surgical planning in drug-resistant focal epilepsy. Functional MRI is widely used as a noninvasive alternative to the Wada test but may be contraindicated or unreliable in some patients, motivating complementary approaches such as magnetoencephalography (MEG). We tested whether beta-band MEG power suppression can provide reliable language lateralization when combined with a task-matched nonlinguistic control, participant/ROI-specific timing, and a multi-threshold weighted-bootstrap laterality index (LI). Seventy-four adults with temporal lobe epilepsy completed visual semantic-decision and false-font control tasks during MEG. Beta-band (17-25 Hz) source power was estimated with DICS beamforming, and LIs were computed in frontal, temporal, angular, and composite lateral ROIs using 300-ms sliding windows. For comparison, participants also completed an auditory semantic-decision versus tone-decision fMRI protocol, with fMRI LIs derived from matched ROIs. MEG showed a left-lateralized spatiotemporal sequence in which frontal LI emerged earlier than temporal-parietal LI, following early bilateral temporo-parieto-occipital engagement. Compared with a prestimulus baseline, the false-font control contrast increased LI magnitude and improved MEG-fMRI correspondence, with a peak MEG-fMRI correlation of r = 0.65. Participant/ROI-specific timing produced only small and mixed changes in concordance. Using the optimized MEG pipeline, MEG and fMRI agreed in 67/74 patients (90.5%; 95% CI: 83-96%; κ = 0.81). Bootstrap LIs performed similarly to magnitude- and vertex-count-based variants while providing confidence intervals. These findings suggest that beta-band MEG suppression during semantic decision, especially when contrasted with a nonlinguistic control task, provides a temporally resolved and clinically feasible marker of hemispheric language dominance that is highly concordant with fMRI.

IntroductionMild-to-moderate traumatic brain injury (mmTBI) that lead to deficits in balance and gait are difficult to resolve through standard therapy protocols, and these deficits can severely impact a patient's quality of life. Recently, translingual neural stimulation (TLNS) has emerged as a potential therapy for mmTBI-related balance and gait deficits by inducing neuroplastic changes in the brain gray matter structure. However, it is still unclear how interactions within and between functional networks in brain are affected by TLNS. The current study aimed to extend our previous resting-state functional connectivity (RSFC) study investigating the effects of TLNS intervention on outcome measures related to gait and balance.MethodsAn experimental PoNS device was utilized to deliver the TLNS. The 2-week TLNS intervention program, specifically stimulation during focused physical therapy focused on recovery of gait and balance, included twice-daily treatment in the laboratory and the same program at home during the intervening weekend. The resting-state fMRI datasets at pre- and post-interventions were collected by 3T MRI scanner with nine mmTBI patients. All participants also received both Sensory Organization Test (SOT) and Dynamic Gait Index (DGI) testing pre- and post-intervention as part of the behavioral assessment.ResultsCompared to baseline, TLNS intervention led to statistically significant improvements in both the SOT [t(8) = 2.742, p = 0.028] and the DGI [t(8) = 2.855, p = 0.024] scores. Moreover, significant increases in intra- and inter-network RSFC were observed, particularly within the visual, default mode, dorsal attention, frontoparietal (FPN), and somatosensory (SMN) networks. Additionally, there were significant correlations between the SOT and inter-network FC [between FPN and SMN, r(9) = –0.784, p = 0.012] and between the DGI and intra-network FC [within SMN, r(9) = 0.728, p = 0.026].DiscussionThese findings suggest that TLNS intervention is an effective in increasing somatosensory processing, vestibular-visual interaction, executive control and flexible shifting, and TLNS may be an effective approach to inducing brain network plasticity and may serve as a potential therapy for mmTBI-related gait and balance deficits.

We introduce a novel, data-driven topological data analysis (TDA) approach for embedding brain networks into a lower-dimensional space in quantifying the dynamics of temporal lobe epilepsy (TLE) obtained from resting-state functional magnetic resonance imaging (rs-fMRI). This embedding facilitates the orthogonal projection of 0D and 1D topological features, allowing for the visualization and modeling of the dynamics of functional human brain networks in a resting state. We then quantify the topological disparities between networks to determine the coordinates for embedding. This framework enables us to conduct a coherent statistical inference within the embedded space. Our results indicate that brain network topology in TLE patients exhibits increased rigidity in 0D topology but more rapid flections compared to that of normal controls in 1D topology.

INTRODUCTION:Emerging evidence illustrates that temporal lobe epilepsy (TLE) involves network disruptions represented by hyperexcitability and other seizure-related neural plasticity. However, these associations are not well-characterized. Our study characterizes the whole brain white matter connectome abnormalities in TLE patients compared to healthy controls (HCs) from the prospective Epilepsy Connectome Project study. Furthermore, we assessed whether aberrant white matter connections are differentially related to cognitive impairment and a history of focal-to-bilateral tonic-clonic (FBTC) seizures. METHODS:Multi-shell connectome MRI data were preprocessed using the DESIGNER guidelines. The IIT Destrieux gray matter atlas was used to derive the 162 × 162 structural connectivity matrices (SCMs) using MRTrix3. ComBat data harmonization was applied to harmonize the SCMs from pre- and post-scanner upgrade acquisitions. Threshold-free network-based statistics were used for statistical analysis of the harmonized SCMs. Cognitive impairment status and FBTC seizure status were then correlated with these findings. RESULTS:We employed connectome measurements from 142 subjects, including 92 patients with TLE (36 males, mean age = 40.1 ± 11.7 years) and 50 HCs (25 males, mean age = 32.6 ± 10.2 years). Our analysis revealed overall significant decreases in cross-sectional area (CSA) of the white matter tract in TLE group compared to controls, indicating decreased white matter tract integrity and connectivity abnormalities in addition to apparent differences in graph theoretic measures of connectivity and network-based statistics. Focal and generalized cognitive impaired TLE patients showcased higher trend-level abnormalities in the white matter connectome via decreased CSA than those with no cognitive impairment. Patients with a positive FBTC seizure history also showed trend-level findings of association via decreased CSA. CONCLUSIONS:Widespread global aberrant white matter connectome changes were observed in TLE patients and characterized by seizure history and cognitive impairment, laying a foundation for future studies to expand on and validate the novel biomarkers and further elucidate TLE's impact on brain plasticity.

AbstractThe relationship between temporal lobe epilepsy and psychopathology has had a long and contentious history with diverse views regarding the presence, nature and severity of emotional–behavioural problems in this patient population. To address these controversies, we take a new person-centred approach through the application of unsupervised machine learning techniques to identify underlying latent groups or behavioural phenotypes. Addressed are the distinct psychopathological profiles, their linked frequency, patterns and severity and the disruptions in morphological and network properties that underlie the identified latent groups. A total of 114 patients and 83 controls from the Epilepsy Connectome Project were administered the Achenbach System of Empirically Based Assessment inventory from which six Diagnostic and Statistical Manual of Mental Disorders-oriented scales were analysed by unsupervised machine learning analytics to identify latent patient groups. Identified clusters were contrasted to controls as well as to each other in order to characterize their association with sociodemographic, clinical epilepsy and morphological and functional imaging network features. The concurrent validity of the behavioural phenotypes was examined through other measures of behaviour and quality of life. Patients overall exhibited significantly higher (abnormal) scores compared with controls. However, cluster analysis identified three latent groups: (i) unaffected, with no scale elevations compared with controls (Cluster 1, 37%); (ii) mild symptomatology characterized by significant elevations across several Diagnostic and Statistical Manual of Mental Disorders-oriented scales compared with controls (Cluster 2, 42%); and (iii) severe symptomatology with significant elevations across all scales compared with controls and the other temporal lobe epilepsy behaviour phenotype groups (Cluster 3, 21%). Concurrent validity of the behavioural phenotype grouping was demonstrated through identical stepwise links to abnormalities on independent measures including the National Institutes of Health Toolbox Emotion Battery and quality of life metrics. There were significant associations between cluster membership and sociodemographic (handedness and education), cognition (processing speed), clinical epilepsy (presence and lifetime number of tonic–clonic seizures) and neuroimaging characteristics (cortical volume and thickness and global graph theory metrics of morphology and resting-state functional MRI). Increasingly dispersed volumetric abnormalities and widespread disruptions in underlying network properties were associated with the most abnormal behavioural phenotype. Psychopathology in these patients is characterized by a series of discrete latent groups that harbour accompanying sociodemographic, clinical and neuroimaging correlates. The underlying neurobiological patterns suggest that the degree of psychopathology is linked to increasingly dispersed abnormal brain networks. Similar to cognition, machine learning approaches support a novel developing taxonomy of the comorbidities of epilepsy.

Temporal lobe epilepsy (TLE) is the most common epilepsy syndrome that empirically represents a network disorder, which makes graph theory (GT) a practical approach to understand it. Multi-shell diffusion-weighted imaging (DWI) was obtained from 89 TLE and 50 controls. GT measures extracted from harmonized DWI matrices were used as factors in a support vector machine (SVM) analysis to discriminate between groups, and in a k-means algorithm to find intrinsic structural phenotypes within TLE. SVM was able to predict group membership (mean accuracy = 0.70, area under the curve (AUC) = 0.747, Brier score (BS) = 0.264) using 10-fold cross-validation. In addition, k-means clustering identified 2 TLE clusters: 1 similar to controls, and 1 dissimilar. Clusters were significantly different in their distribution of cognitive phenotypes, with the Dissimilar cluster containing the majority of TLE with cognitive impairment (χ2 = 6.641, P = 0.036). In addition, cluster membership showed significant correlations between GT measures and clinical variables. Given that SVM classification seemed driven by the Dissimilar cluster, SVM analysis was repeated to classify Dissimilar versus Similar + Controls with a mean accuracy of 0.91 (AUC = 0.957, BS = 0.189). Altogether, the pattern of results shows that GT measures based on connectome DWI could be significant factors in the search for clinical and neurobehavioral biomarkers in TLE.

OBJECTIVE:Social determinants of health, including the effects of neighborhood disadvantage, impact epilepsy prevalence, treatment, and outcomes. This study characterized the association between aberrant white matter connectivity in temporal lobe epilepsy (TLE) and disadvantage using a US census-based neighborhood disadvantage metric, the Area Deprivation Index (ADI), derived from measures of income, education, employment, and housing quality. METHODS:Participants including 74 TLE patients (47 male, mean age = 39.2 years) and 45 healthy controls (27 male, mean age = 31.9 years) from the Epilepsy Connectome Project were classified into ADI-defined low and high disadvantage groups. Graph theoretic metrics were applied to multishell connectome diffusion-weighted imaging (DWI) measurements to derive 162 × 162 structural connectivity matrices (SCMs). The SCMs were harmonized using neuroCombat to account for interscanner differences. Threshold-free network-based statistics were used for analysis, and findings were correlated with ADI quintile metrics. A decrease in cross-sectional area (CSA) indicates reduced white matter integrity. RESULTS:Sex- and age-adjusted CSA in TLE groups was significantly reduced compared to controls regardless of disadvantage status, revealing discrete aberrant white matter tract connectivity abnormalities in addition to apparent differences in graph measures of connectivity and network-based statistics. When comparing broadly defined disadvantaged TLE groups, differences were at trend level. Sensitivity analyses of ADI quintile extremes revealed significantly lower CSA in the most compared to least disadvantaged TLE group. SIGNIFICANCE:Our findings demonstrate (1) the general impact of TLE on DWI connectome status is larger than the association with neighborhood disadvantage; however, (2) neighborhood disadvantage, indexed by ADI, revealed modest relationships with white matter structure and integrity on sensitivity analysis in TLE. Further studies are needed to explore this relationship and determine whether the white matter relationship with ADI is driven by social drift or environmental influences on brain development. Understanding the etiology and course of the disadvantage-brain integrity relationship may serve to inform care, management, and policy for patients.

Short-range functional connectivity in the limbic network is increased in patients with temporal lobe epilepsy (TLE), and recent studies have shown that cortical myelin content correlates with fMRI connectivity. We thus hypothesized that myelin may increase progressively in the epileptic network. We compared T1w/T2w gray matter myelin maps between TLE patients and age-matched controls and assessed relationships between myelin and aging. While both TLE patients and healthy controls exhibited increased T1w/T2w intensity with age, we found no evidence for significant group-level aberrations in overall myelin content or myelin changes through time in TLE.

Persistent homology offers a powerful tool for extracting hidden topological signals from brain networks. It captures the evolution of topological structures across multiple scales, known as filtrations, thereby revealing topological features that persist over these scales. These features are summarized in persistence diagrams, and their dissimilarity is quantified using the Wasserstein distance. However, the Wasserstein distance does not follow a known distribution, posing challenges for the application of existing parametric statistical models. To tackle this issue, we introduce a unified topological inference framework centered on the Wasserstein distance. Our approach has no explicit model and distributional assumptions. The inference is performed in a completely data driven fashion. We apply this method to resting-state functional magnetic resonance images (rs-fMRI) of temporal lobe epilepsy patients collected from two different sites: the University of Wisconsin-Madison and the Medical College of Wisconsin. Importantly, our topological method is robust to variations due to sex and image acquisition, obviating the need to account for these variables as nuisance covariates. We successfully localize the brain regions that contribute the most to topological differences. A MATLAB package used for all analyses in this study is available at https://github.com/laplcebeltrami/PH-STAT.

We propose a unique, minimal assumption, approach based on variance analyses (compared with standard approaches) to investigate genetic influence on individual differences on the functional connectivity of the brain using 65 monozygotic and 65 dizygotic healthy young adult twin pairs' low‐frequency oscillation resting state functional Magnetic Resonance Imaging (fMRI) data from the Human Connectome Project. Overall, we found high number of genetically‐influenced functional (GIF) connections involving posterior to posterior brain regions (occipital/temporal/parietal) implicated in low‐level processes such as vision, perception, motion, categorization, dorsal/ventral stream visuospatial, and long‐term memory processes, as well as high number across midline brain regions (cingulate) implicated in attentional processes, and emotional responses to pain. We found low number of GIF connections involving anterior to anterior/posterior brain regions (frontofrontal > frontoparietal, frontotemporal, frontooccipital) implicated in high‐level processes such as working memory, reasoning, emotional judgment, language, and action planning. We found very low number of GIF connections involving subcortical/noncortical networks such as basal ganglia, thalamus, brainstem, and cerebellum. In terms of sex‐specific individual differences, individual differences in males were more genetically influenced while individual differences in females were more environmentally influenced in terms of the interplay of interactions of Task positive networks (brain regions involved in various task‐oriented processes and attending to and interacting with environment), extended Default Mode Network (a central brain hub for various processes such as internal monitoring, rumination, and evaluation of self and others), primary sensorimotor systems (vision, audition, somatosensory, and motor systems), and subcortical/noncortical networks. There were >8.5‐19.1 times more GIF connections in males than females. These preliminary (young adult cohort‐specific) findings suggest that individual differences in the resting state brain may be more genetically influenced in males and more environmentally influenced in females; furthermore, standard approaches may suggest that it is more substantially nonadditive genetics, rather than additive genetics, which contribute to the differences in sex‐specific individual differences based on this young adult (male and female) specific cohort. Finally, considering the preliminary cohort‐specific results, based on standard approaches, environmental influences on individual differences may be substantially greater than that of genetics, for either sex, frontally and brain‐wide. [Correction added on 10 May 2023, after first online publication: added: functional Magnetic Resonance Imaging. Added: individual differences in, twice. Added statement between furthermore … based on standard approaches.]

Growing evidence suggests that psychopathy is related to altered connectivity within and between three largescale brain networks that support core cognitive functions, including allocation of attention. In healthy individuals, default mode network (DMN) is involved in internally-focused attention and cognition such as selfreference. Frontoparietal network (FPN) is anticorrelated with DMN and is involved in externally-focused attention to cognitively demanding tasks. A third network, salience network (SN), is involved in detecting salient cues and, crucially, appears to play a role in switching between the two anticorrelated networks, DMN and FPN, to efficiently allocate attentional resources. Psychopathy has been related to reduced anticorrelation between DMN and FPN, suggesting SN's role in switching between these two networks may be diminished in the disorder. To test this hypothesis, we used independent component analysis to derive DMN, FPN, and SN activity in resting-state fMRI data in a sample of incarcerated men (N = 148). We entered the activity of the three networks into dynamic causal modeling to test SN's switching role. The previously established switching effect of SN among young, healthy adults was replicated in a group of low psychopathy participants (posterior model probability = 0.38). As predicted, SN's switching role was significantly diminished in high psychopathy participants (t(145) = 26.39, p < .001). These findings corroborate a novel theory of brain function in psychopathy. Future studies may use this model to test whether disrupted SN switching is related to high psychopathy individuals' abnormal allocation of attention.

PET and fMRI studies suggest that auditory narrative comprehension is supported by a bilateral multilobar cortical network. The superior temporal resolution of magnetoencephalography (MEG) makes it an attractive tool to investigate the dynamics of how different neuroanatomic substrates engage during narrative comprehension. Using beta-band power changes as a marker of cortical engagement, we studied MEG responses during an auditory story comprehension task in 31 healthy adults. The protocol consisted of two runs, each interleaving 7 blocks of the story comprehension task with 15 blocks of an auditorily presented math task as a control for phonological processing, working memory, and attention processes. Sources at the cortical surface were estimated with a frequency-resolved beamformer. Beta-band power was estimated in the frequency range of 16-24 Hz over 1-sec epochs starting from 400 msec after stimulus onset until the end of a story or math problem presentation. These power estimates were compared to 1-second epochs of data before the stimulus block onset. The task-related cortical engagement was inferred from beta-band power decrements. Group-level source activations were statistically compared using non-parametric permutation testing. A story-math contrast of beta-band power changes showed greater bilateral cortical engagement within the fusiform gyrus, inferior and middle temporal gyri, parahippocampal gyrus, and left inferior frontal gyrus (IFG) during story comprehension. A math-story contrast of beta power decrements showed greater bilateral but left-lateralized engagement of the middle frontal gyrus and superior parietal lobule. The evolution of cortical engagement during five temporal windows across the presentation of stories showed significant involvement during the first interval of the narrative of bilateral opercular and insular regions as well as the ventral and lateral temporal cortex, extending more posteriorly on the left and medially on the right. Over time, there continued to be sustained right anterior ventral temporal engagement, with increasing involvement of the right anterior parahippocampal gyrus, STG, MTG, posterior superior temporal sulcus, inferior parietal lobule, frontal operculum, and insula, while left hemisphere engagement decreased. Our findings are consistent with prior imaging studies of narrative comprehension, but in addition, they demonstrate increasing right-lateralized engagement over the course of narratives, suggesting an important role for these right-hemispheric regions in semantic integration as well as social and pragmatic inference processing.

Determining genetic versus environmental influences on the human brain is of crucial importance to understand the healthy brain as well as in a variety of disease and disorder states. Here we propose a unique, minimal assumption, approach to investigate genetic influence on the functional connectivity of the brain using 260 subjects” (65 monozygotic (MZ) and 65 dizygotic (DZ) healthy young adult twin pairs) resting state fMRI (rsfMRI) data from the Human Connectome Project (HCP). For any given resting state connection between twin pairs, the connection strengths across pairs were subtracted from each other in both directions. By applying the F-Test for equality of variances per connection, we found that there were a number of significant connections that demonstrated greater variance among dizygotic pairs in comparison to monozygotic pairs, implying these connections were under significant genetic influence. These population (DZ-MZ) results remained true irrespective of gender, with the caveat that certain connections were significant on a gender-specific basis. This is the first study to our knowledge to assess the heritability across young healthy adults both in general and specific to gender. Population Results & Discussion At the population level, there appears to be a posterior to anterior gradient of more to less genetic influence on brain connections and networks with visual > temporal, parietal > frontal. There was a high density of genetically-influenced functional connections predominantly involving posterior regions or networks of the brain: Visual Networks (VNs - primary visual, early visual, dorsal stream and ventral stream visual cortices, MT+ complex). These posterior regions of the brain with greater genetic influence are implicated for example in visual, perceptual, dorsal (“where”) and ventral (“what”) visuospatial processing streams (VNs). There was a low-density or paucity of genetically-influenced functional connections predominantly involving anterior regions or networks of the brain comprising Task Positive Networks (TPNs): FrontoParietal Networks (FPNs - dorsolateral prefrontal, orbital and polar frontal, midcingulate, insular and frontal opercular, superior and inferior parietal cortices); FrontoTemporal Networks (FTNs - inferior frontal, posterior opercular, early auditory, auditory association cortices); Sensorimotor Networks (SMNs - premotor, somatosensory, paralobular, and motor cortices); These anterior regions of the brain with lesser genetic influence are implicated in various TPN processes; for example in high-level cognitive and affective processes such as working memory, executive function, reasoning, attentional and impulse control, emotional judgement and decision making (FPNs); language and auditory processes (FTNs); action-planning and movement processes (SMN). There was a mix of high (posterior) and low (anterior) density of genetically influenced functional connections involving the extended Default Mode Network (eDMN). Specifically, there was a high density of genetically-influenced functional connections involving predominantly posterior-medial regions of eDMN - hippocampus and precuneus/posterior cingulate cortices; There was a low density of genetically influenced connections involving anterior regions (anterior cingulate and medial prefrontal) and lateral (inferior parietal, temporoparietooccipital) regions of the eDMN. The eDMN is involved in low-level cognitive and affective processes such as those involved in episodic memory retrieval, mental imagery, introspection, rumination, evaluation of self and others. These differences in genetic influence on posterior (more) vs. anterior (less) brain regions may have implications in terms of the environmental influence (e.g., education, school and work environment, family and home environment, social interaction with friends and peers, medications, nutrition, sports and physical exercise) on posterior (less) vs. anterior (more) portions of the brain during development and later in life. Gender-Specific Results & Discussion As noted at the population level, both males and females were under extensive genetic influence in terms of network interactions involving visual cortices. In addition, males were more genetically influenced in terms of network interactions involving auditory-language related cortices compared to females. This finding suggests that males may be more functionally “hard-wired” and females may be more environmentally influenced and shaped in terms of auditory-language systems than males. As noted at the population level, both males and females were under extensive genetic influence in terms of interactions involving the eDMN which is considered a central hub of the brain for various processes such as internal monitoring, rumination and evaluation of self and others, as noted previously. In addition, males also were more genetically influenced compared to females in terms of intranetwork and internetwork interactions of eDMN and other brain regions (occipital, temporal, parietal, and frontal regions) involved in various task-oriented processes and attending to and interacting with the environment which comprise part of the Task Positive Networks (TPNs). There were also nearly five times more genetically influenced functional connections in males (310) than females (64) suggesting that male brains are more genetically influenced, i.e. functionally “hard-wired”, than females. This result suggests differences in genetic predisposition in males (more) vs. females (less) in terms of interplay of attending to task-oriented interactions with the environment (TPNs) vs. internal and external interactions with self and others (eDMN). This finding may also have implications in terms of brain plasticity differences in males (less) versus females (more) in terms of ability to react or adapt/maladapt to environmental influences (e.g. task completion demands, psychosocial stressors, positive and negative feedback, meditation, cognitive behavioral therapy, pharmacotherapy) and their overall malleability. These results reveal the similarities and differences of genetics and environmental influences on different connections, areas, and networks of the resting state functional brain in young healthy males and females with implications in development and later in life. This unique method can be applied in healthy as well as in patient populations to reveal the genetic and environmental influences on the brain. Significance There were high vs. low genetic influences on posterior vs. anterior brain regions involved in low-level visuospatial processes vs. high-level cognitive processes such as reasoning and language respectively. This finding may have implications in terms of the brain to be environmentally influenced (e.g., school, work and home environment) during development and later in life. There were nearly five times more genetically influenced functional connections in males than females in brain regions involved in task-oriented interactions with environment vs. interactions with self and others. This finding may have implications in terms of brain plasticity differences in males (less) versus females (more) in terms of ability to adapt/maladapt to environmental influences (e.g. task completion demands, psychosocial stressors, various therapies) and their overall malleability. This is the first study to our knowledge to assess the heritability across young healthy adults both in general and specific to gender.

Temporal lobe epilepsy (TLE) has been conceptualized as focal disease with a discrete neurobiological focus and can respond well to targeted resection or ablation. In contrast, the neuro-cognitive deficits resulting from TLE can be widespread involving regions beyond the primary epileptic network. We hypothesize that this seemingly paradoxical findings can be explained by differences in connectivity between the primary epileptic region which is hyper-connected and its secondary influence on global connectome organization. This hypothesis is tested using regional and global graph theory metrics where we anticipate that regional mesial-temporal hyperconnectivity will be found and correlate with seizure frequency while global networks will be disorganized and be more closely associated with neurocognitive deficits. Resting-state fMRI was used to examine temporal lobe regional connectivity and global functional connectivity from 102 patients with TLE and 55 controls. Connectivity matrices were calculated for subcortical volumes and cortical parcellations. Graph theory metrics (global clustering coefficient (GCC), degree, closeness) were compared between groups and in relation to neuropsychological profiles and disease covariates using permutation testing and causal analysis. In TLE there was a decrease in GCC (p = 0.0345) associated with a worse neuropsychological profile (p = 0.0134). There was increased connectivity in the left hippocampus/amygdala (degree p = 0.0103, closeness p = 0.0104) and a decrease in connectivity in the right lateral temporal lobe (degree p = 0.0186, closeness p = 0.0122). A ratio between the hippocampus/amygdala and lateral temporal lobe-temporal lobe connectivity ratio (TLCR) revealed differences between TLE and controls for closeness (left p = 0.00149, right p = 0.0494) and for degree on left p = 0.00169; with trend on right p = 0.0567. Causal analysis suggested that "Epilepsy Activity" (seizure frequency, anti-seizure medications) was associated with increase in TLCR but not in GCC, while cognitive decline was associated with decreased GCC. These findings support the hypothesis that in TLE there is hyperconnectivity in the hippocampus/amygdala and hypoconnectivity in the lateral temporal lobe associated with "Epilepsy Activity." While, global connectome disorganization was associated with worse neuropsychological phenotype. (C) 2021 Elsevier Inc. All rights reserved.

Background: Depression is associated with altered functional connectivity and altered cortisol sensitivity, but the effects of cortisol on functional connectivity in depression are unknown. Previous research shows that brief cortisol augmentation (CORT) has beneficial neurocognitive effects in depression. Methods: We investigated the effects of CORT (20mg oral cortisol) on functional connectivity during emotion processing in women with depression. Participants included 75 women with no depression or a depressive disorder. In a double-blind, crossover study, we used functional magnetic resonance imaging to measure effects of CORT vs. placebo on task-based functional connectivity during presentation of emotionally-laden images. We performed psychophysiological interaction (PPI) to test interactions among depression severity, cortisol administration, and task-dependent functional connectivity using the hippocampus and amygdala as seeds. Results: During the presentation of negative images, CORT (vs. placebo) increased functional connectivity between the hippocampus and putamen in association with depression severity. During the presentation of positive pictures CORT increased functional connectivity between the hippocampus and middle frontal gyrus as well as superior temporal gyrus in association with depression. Limitations: Because cortisol was pharmacologically manipulated, results cannot be extrapolated to endogenous increases in cortisol levels. The sample did not permit investigation of differences due to race, ethnicity, or sex. Co-morbidities such as anxiety or PTSD were not accounted for. Conclusions: The results suggest that CORT has normalizing effects on task-dependent functional connectivity in women with depression during emotion processing. Increasing cortisol availability or signaling may have therapeutic benefits within affective disorders.

As a powerful technique in medical imaging, image synthesis is widely used in applications such as denoising, super resolution and modality transformation etc. Recently, the revival of deep neural networks made immense progress in the field of medical imaging. Although many deep leaning based models have been proposed to improve the image synthesis accuracy, the evaluation of the model uncertainty, which is highly important for medical applications, has been a missing part. In this work, we propose to use Bayesian conditional generative adversarial network (GAN) with concrete dropout to improve image synthesis accuracy. Meanwhile, an uncertainty calibration approach is involved in the whole pipeline to make the uncertainty generated by Bayesian network interpretable. The method is validated with the T1w to T2w MR image translation with a brain tumor dataset of 102 subjects. Compared with the conventional Bayesian neural network with Monte Carlo dropout, results of the proposed method reach a significant lower RMSE with a p-value of 0.0186. Improvement of the calibration of the generated uncertainty by the uncertainty recalibration method is also illustrated.

The association of epilepsy with structural brain changes and cognitive abnormalities in midlife has raised concern regarding the possibility of future accelerated brain and cognitive aging and increased risk of later life neurocognitive disorders. To address this issue we examined age-related processes in both structural and functional neuroimaging among individuals with temporal lobe epilepsy (TLE, N = 104) who were participants in the Epilepsy Connectome Project (ECP). Support vector regression (SVR) models were trained from 151 healthy controls and used to predict TLE patients' brain ages. It was found that TLE patients on average have both older structural (+ 6.6 years) and functional (+ 8.3 years) brain ages compared to healthy controls. Accelerated functional brain age (functional - chronological age) was mildly correlated (corrected P = 0.07) with complex partial seizure frequency and the number of anti-epileptic drug intake. Functional brain age was a significant correlate of declining cognition (fluid abilities) and partially mediated chronological age-fluid cognition relationships. Chronological age was the only positive predictor of crystallized cognition. Accelerated aging is evident not only in the structural brains of patients with TLE, but also in their functional brains. Understanding the causes of accelerated brain aging in TLE will be clinically important in order to potentially prevent or mitigate their cognitive deficits.

Neuroticism, a core personality trait characterized by a tendency towards experiencing negative affect, has been reported to be higher in people with temporal lobe epilepsy (TLE) compared with healthy individuals. Neuroticism is a known predictor of depression and anxiety, which also occur more frequently in people with TLE. The purpose of this study was to identify abnormalities in whole-brain resting-state functional connectivity in relation to neuroticism in people with TLE and to determine the degree of unique versus shared patterns of abnormal connectivity in relation to elevated symptoms of depression and anxiety. Ninety-three individuals with TLE (55 females) and 40 healthy controls (18 females) from the Epilepsy Connectome Project (ECP) completed measures of neuroticism, depression, and anxiety, which were all significantly higher in people with TLE compared with controls. Resting-state functional connectivity was compared between controls and groups with TLE with high and low neuroticism using analysis of variance (ANOVA) and t-test. In secondary analyses, the same analytics were performed using measures of depression and anxiety and the unique variance in resting-state connectivity associated with neuroticism independent of symptoms of depression and anxiety identified. Increased neuroticism was significantly associated with hyposynchrony between the right hippocampus and Brodmann area (BA) 9 (region of prefrontal cortex (PFC)) (p < 0.005), representing a unique relationship independent of symptoms of depression and anxiety. Hyposynchrony of connection between the right hippocampus and BA47 (anterior frontal operculum) was associated with high neuroticism and with higher depression and anxiety scores (p < 0.05), making it a shared abnormal connection for the three measures. In conclusion, increased neuroticism exhibits both unique and shared patterns of abnormal functional connectivity with depression and anxiety symptoms between regions of the mesial temporal and frontal lobe.

Understanding how global brain networks are affected in epilepsy may elucidate the pathogenesis of seizures and its accompanying neurobehavioral comorbidities. We investigated functional changes within neural networks in temporal lobe epilepsy (TLE) using graph theory analysis of resting-state connectivity. Twenty-seven TLE presurgical patients (age 41.0 +/- 12.3 years) and 85 age, gender, and handedness equivalent healthy controls (HCs; age 39.7 +/- 16.9 years) were enrolled. Eyes-closed resting-state functional magnetic resonance image scans were analyzed to compare network properties and functional connectivity (FC) changes. TLE subjects showed significantly higher global efficiency, lower clustering coefficient ratio, and lower shortest path lengths ratio than HCs, as an indication of a more synchronized, yet less segregated network. A trend of functional reorganization with a shift of network hubs to the contralateral hemisphere was noted in TLE subjects. Support vector machine (SVM) with linear kernel was trained to separate between neural networks in TLE and HC subjects based on graph measurements. SVM analysis allowed separation between TLE and HC networks with 80.66% accuracy using eight features of graph measurements. Support vector regression (SVR) was used to predict neurocognitive performance from graph metrics. An SVR linear predictor showed discriminative prediction accuracy for four key neurocognitive variables in TLE (absolute R value range: 0.61-0.75). Despite TLE, our results showed both local and global network topology differences that reflect widespread alterations in FC in TLE. Network differences are discriminative between TLE and HCs using data-driven analysis and predicted severity of neurocognitive sequelae in our cohort.