Impaired topology and connectivity of grey matter structural networks in major depressive disorder: evidence from a multi-site neuroimaging data-set.

BACKGROUND: Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD. AIMS: Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes. METHOD: A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings. RESULTS: Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms. CONCLUSIONS: Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.

Resting-state global brain activity affects early ß-amyloid accumulation in default mode network.

It remains unclear why ß-amyloid (Aß) plaque, a hallmark pathology of Alzheimer's disease (AD), first accumulates cortically in the default mode network (DMN), years before AD diagnosis. Resting-state low-frequency ( < 0.1 Hz) global brain activity recently was linked to AD, presumably due to its role in glymphatic clearance. Here we show that the preferential Aß accumulation in the DMN at the early stage of Aß pathology was associated with the preferential reduction of global brain activity in the same regions. This can be partly explained by its failure to reach these regions as propagating waves. Together, these findings highlight the important role of resting-state global brain activity in early preferential Aß deposition in the DMN.

White matter hyperintensities influence distal cortical ß-amyloid accumulation in default mode network pathways.

BACKGROUND AND PURPOSE: Cerebral small vessel disease (SVD) has been suggested to contribute to the pathogenesis of Alzheimer's disease (AD). Yet, the role of SVD in potentially contributing to AD pathology is unclear. The main objective of this study was to test the hypothesis that WMHs influence amyloid ß (Aß) levels within connected default mode network (DMN) tracts and cortical regions in cognitively unimpaired older adults. METHODS: Regional standard uptake value ratios (SUVr) from Aß-PET and white matter hyperintensity (WMH) volumes from three-dimensional magnetic resonance imaging FLAIR images were analyzed across a sample of 72 clinically unimpaired (mini-mental state examination ≥26), older adults (mean age 74.96 and standard deviation 8.13) from the Alzheimer's Disease Neuroimaging Initiative (ADNI3). The association of WMH volumes in major fiber tracts projecting from cortical DMN regions and Aß-PET SUVr in the connected cortical DMN regions was analyzed using linear regression models adjusted for age, sex, ApoE, and total brain volumes. RESULTS: The regression analyses demonstrate that increased WMH volumes in the superior longitudinal fasciculus were associated with increased regional SUVr in the inferior parietal lobule (p = .011). CONCLUSION: The findings suggest that the relation between Aß in parietal cortex is associated with SVD in downstream white matter (WM) pathways in preclinical AD. The biological relationships and interplay between Aß and WM microstructure alterations that precede overt WMH development across the continuum of AD progression warrant further study.

Posterior cingulate cortex targeted real-time fMRI neurofeedback recalibrates functional connectivity with the amygdala, posterior insula, and default-mode network in PTSD.

BACKGROUND: Alterations within large-scale brain networks-namely, the default mode (DMN) and salience networks (SN)-are present among individuals with posttraumatic stress disorder (PTSD). Previous real-time functional magnetic resonance imaging (fMRI) and electroencephalography neurofeedback studies suggest that regulating posterior cingulate cortex (PCC; the primary hub of the posterior DMN) activity may reduce PTSD symptoms and recalibrate altered network dynamics. However, PCC connectivity to the DMN and SN during PCC-targeted fMRI neurofeedback remains unexamined and may help to elucidate neurophysiological mechanisms through which these symptom improvements may occur. METHODS: Using a trauma/emotion provocation paradigm, we investigated psychophysiological interactions over a single session of neurofeedback among PTSD (n = 14) and healthy control (n = 15) participants. We compared PCC functional connectivity between regulate (in which participants downregulated PCC activity) and view (in which participants did not exert regulatory control) conditions across the whole-brain as well as in a priori specified regions-of-interest. RESULTS: During regulate as compared to view conditions, only the PTSD group showed significant PCC connectivity with anterior DMN (dmPFC, vmPFC) and SN (posterior insula) regions, whereas both groups displayed PCC connectivity with other posterior DMN areas (precuneus/cuneus). Additionally, as compared with controls, the PTSD group showed significantly greater PCC connectivity with the SN (amygdala) during regulate as compared to view conditions. Moreover, linear regression analyses revealed that during regulate as compared to view conditions, PCC connectivity to DMN and SN regions was positively correlated to psychiatric symptoms across all participants. CONCLUSION: In summary, observations of PCC connectivity to the DMN and SN provide emerging evidence of neural mechanisms underlying PCC-targeted fMRI neurofeedback among individuals with PTSD. This supports the use of PCC-targeted neurofeedback as a means by which to recalibrate PTSD-associated alterations in neural connectivity within the DMN and SN, which together, may help to facilitate improved emotion regulation abilities in PTSD.

Prenatal depressive symptoms and childhood development of brain limbic and default mode network structure.

Prenatal depressive symptoms are linked to negative child behavioral and cognitive outcomes and predict later psychopathology in adolescent children. Prior work links prenatal depressive symptoms to child brain structure in regions like the amygdala; however, the relationship between symptoms and the development of brain structure over time remains unclear. We measured maternal depressive symptoms during pregnancy and acquired longitudinal T1-weighted and diffusion imaging data in children (n = 111; 60 females) between 2.6 and 8 years of age. Controlling for postnatal symptoms, we used linear mixed effects models to test relationships between prenatal depressive symptoms and age-related changes in (i) amygdala and hippocampal volume and (ii) structural properties of the limbic and default-mode networks using graph theory. Higher prenatal depressive symptoms in the second trimester were associated with more curvilinear trajectories of left amygdala volume changes. Higher prenatal depressive symptoms in the third trimester were associated with slower age-related changes in limbic global efficiency and average node degree across childhood. Our work provides evidence that moderate symptoms of prenatal depression in a low sociodemographic risk sample are associated with structural brain development in regions and networks implicated in emotion processing.

Blood-brain barrier opening of the default mode network in Alzheimer's disease with magnetic resonance-guided focused ultrasound.

The blood-brain barrier (BBB) protects the brain but is also an important obstacle for the effective delivery of therapeutics in Alzheimer's disease and other neurodegenerative disorders. Transcranial magnetic resonance-guided focused ultrasound (MRgFUS) has been shown to reversibly disrupt the BBB. However, treatment of diffuse regions across the brain along with the effect on Alzheimer's disease relevant pathology need to be better characterized. This study is an open-labelled single-arm trial (NCT04118764) to investigate the feasibility of modulating BBB permeability in the default mode network and the impact on cognition, amyloid and tau pathology as well as BBB integrity. Nine participants [mean age 70.2 ± 7.2 years, mean Mini-Mental State Examination (MMSE) 21.9] underwent three biweekly procedures with follow-up visits up to 6 months. The BBB permeability of the bilateral hippocampi, anterior cingulate cortex and precuneus was transiently increased without grade 3 or higher adverse events. Participants did not experience worsening trajectory of cognitive decline (ADAS-cog11, MMSE). Whole brain vertex-based analysis of the 18F-florbetaben PET imaging demonstrated clusters of modest SUVR reduction in the right parahippocampal and inferior temporal lobe. However, CSF and blood biomarkers did not demonstrate any amelioration of Alzheimer's disease pathology (P-tau181, amyloid-ß42/40 ratio), nor did it show persistent BBB dysfunction (plasma PDGFRbeta and CSF-to-plasma albumin ratio). This study provides neuroimaging and fluid biomarker data to characterize the safety profile of MRgFUS BBB modulation in neurodegeneration as a potential strategy for enhanced therapeutic delivery.

Language reorganization in patients with left-hemispheric gliomas is associated with increased cortical volume in language-related areas and in the default mode network.

BACKGROUND: Language function may reorganize to overcome focal impairment; however, the relation between functional and structural changes in patients with brain tumors remains unclear. We investigated the cortical volume of atypical language dominant (AD) patients with left frontal-insular high-grade (HGG) and low-grade glioma (LGG). We hypothesized atypical language being associated with areas of increased cortical volume in the right hemisphere, including language areas homologues. METHODS: Patient were recruited following the criteria: left frontal-insular glioma; functional MRI and 3DT1-weighted images; no artifacts. We calculated an hemispheric language laterality index (LI), defined as: AD if LI < .2; left-dominant (LD) if LI ≥ .2. We measured cortical volume in three voxel-based morphometry (VBM) analyses: total AD vs. LD patients; AD vs. LD in HGG; AD vs. LD in LGG. We repeated the analysis in AD vs. LD healthy controls (HC). A minimum threshold of t > 2 and corrected p < .025 (Bonferroni) was employed. RESULTS: We recruited 119 patients (44LGG, 75HGG). Hemispheric LI demonstrated 64/119AD and 55/119LD patients. The first VBM analysis demonstrated significantly increased cortical volume in AD patients in the right inferior frontal gyrus (IFG), right superior temporal gyrus (STG), right insula, right fusiform gyrus (FG), right precentral gyrus, right temporal-parietal junction, right posterior cingulate cortex (PCC), right hippocampus, right- and left cerebellum. AD patients with HGG showed the same areas of significantly increased cortical volume. AD patients with LGG displayed significantly increased cortical volume in right IFG, right STG, right insula, right FG, right anterior cingulate cortex, right PCC, right dorsal-lateral prefrontal cortex. HC showed no significant results. CONCLUSION: Right-sided (atypical) language activations in patients with left-hemispheric gliomas are associated with areas of increased cortical volume. Additionally, default mode network nodes showed greater cortical volume in AD patients regardless of the tumor grade, supporting the idea of these cortices participating in the development of language plasticity.

Exploring brain connectivity changes in major depressive disorder using functional-structural data fusion: A CAN-BIND-1 study.

There is a growing interest in examining the wealth of data generated by fusing functional and structural imaging information sources. These approaches may have clinical utility in identifying disruptions in the brain networks that underlie major depressive disorder (MDD). We combined an existing software toolbox with a mathematically dense statistical method to produce a novel processing pipeline for the fast and easy implementation of data fusion analysis (FATCAT-awFC). The novel FATCAT-awFC pipeline was then utilized to identify connectivity (conventional functional, conventional structural and anatomically weighted functional connectivy) changes in MDD patients compared to healthy comparison participants (HC). Data were acquired from the Canadian Biomarker Integration Network for Depression (CAN-BIND-1) study. Large-scale resting-state networks were assessed. We found statistically significant anatomically-weighted functional connectivity (awFC) group differences in the default mode network and the ventral attention network, with a modest effect size (d < 0.4). Functional and structural connectivity seemed to overlap in significance between one region-pair within the default mode network. By combining structural and functional data, awFC served to heighten or reduce the magnitude of connectivity differences in various regions distinguishing MDD from HC. This method can help us more fully understand the interconnected nature of structural and functional connectivity as it relates to depression.

Topologically convergent and divergent morphological gray matter networks in early-stage Parkinson's disease with and without mild cognitive impairment.

Patients with Parkinson's disease with mild cognitive impairment (PD-M) progress to dementia more frequently than those with normal cognition (PD-N), but the underlying neurobiology remains unclear. This study aimed to define the specific morphological brain network alterations in PD-M, and explore their potential diagnostic value. Twenty-four PD-M patients, 17 PD-N patients, and 29 healthy controls (HC) underwent a structural MRI scan. Similarity between interregional gray matter volume distributions was used to construct individual morphological brain networks. These were analyzed using graph theory and network-based statistics (NBS), and their relationship to neuropsychological tests was assessed. Support vector machine (SVM) was used to perform individual classification. Globally, compared with HC, PD-M showed increased local efficiency (p = .001) in their morphological networks, while PD-N showed decreased normalized path length (p = .008). Locally, similar nodal deficits were found in the rectus and lingual gyrus, and cerebellum of both PD groups relative to HC; additionally in PD-M nodal deficits involved several frontal and parietal regions, correlated with cognitive scores. NBS found that similar connections were involved in the default mode and cerebellar networks of both PD groups (to a greater extent in PD-M), while PD-M, but not PD-N, showed altered connections involving the frontoparietal network. Using connections identified by NBS, SVM allowed discrimination with high accuracy between PD-N and HC (90%), PD-M and HC (85%), and between the two PD groups (65%). These results suggest that default mode and cerebellar disruption characterizes PD, more so in PD-M, whereas frontoparietal disruption has diagnostic potential.

Selective vulnerability to atrophy in sporadic Creutzfeldt-Jakob disease.

OBJECTIVE: Identification of brain regions susceptible to quantifiable atrophy in sporadic Creutzfeldt-Jakob disease (sCJD) should allow for improved understanding of disease pathophysiology and development of structural biomarkers that might be useful in future treatment trials. Although brain atrophy is not usually present by visual assessment of MRIs in sCJD, we assessed whether using voxel-based morphometry (VBM) can detect group-wise brain atrophy in sCJD. METHODS: 3T brain MRI data were analyzed with VBM in 22 sCJD participants and 26 age-matched controls. Analyses included relationships of regional brain volumes with major clinical variables and dichotomization of the cohort according to expected disease duration based on prion molecular classification (i.e., short-duration/Fast-progressors (MM1, MV1, and VV2) vs. long-duration/Slow-progressors (MV2, VV1, and MM2)). Structural equation modeling (SEM) was used to assess network-level interactions of atrophy between specific brain regions. RESULTS: sCJD showed selective atrophy in cortical and subcortical regions overlapping with all but one region of the default mode network (DMN) and the insulae, thalami, and right occipital lobe. SEM showed that the effective connectivity model fit in sCJD but not controls. The presence of visual hallucinations correlated with right fusiform, bilateral thalami, and medial orbitofrontal atrophy. Interestingly, brain atrophy was present in both Fast- and Slow-progressors. Worse cognition was associated with bilateral mesial frontal, insular, temporal pole, thalamus, and cerebellum atrophy. INTERPRETATION: Brain atrophy in sCJD preferentially affects specific cortical and subcortical regions, with an effective connectivity model showing strength and directionality between regions. Brain atrophy is present in Fast- and Slow-progressors, correlates with clinical findings, and is a potential biomarker in sCJD.

Integrated phylogeny of the human brain and pathobiology of Alzheimer's disease: A unifying hypothesis.

The disproportionate evolutionary expansion of the human cerebral cortex with reinforcement of cholinergic innervations warranted a major rise in the functional and metabolic load of the conserved basal forebrain (BF) cholinergic system. Given that acetylcholine (ACh) regulates properties of the microtubule-associated protein (MAP) tau and promotes non-amyloidogenic processing of amyloid precursor protein (APP), growing neocortex predicts higher demands for ACh, while the emerging role of BF cholinergic projections in Aß clearance infers greater exposure of source neurons and their innervation fields to amyloid pathology. The higher exposure of evolutionary most recent cortical areas to the amyloid pathology of Alzheimer's disease (AD) with synaptic impairments and atrophy, therefore, might involve attenuated homeostatic effects of BF cholinergic projections, in addition to fall-outs of inherent processes of expanding association areas. This unifying model, thus, views amyloid pathology and loss of cholinergic cells as a quid pro quo of the allometric evolution of the human brain, which in combination with increase in life expectancy overwhelm the fine homeostatic balance and trigger the disease process.

Whole-brain computational modeling reveals disruption of microscale brain dynamics in HIV infected individuals.

MRI-based neuroimaging techniques have been used to investigate brain injury associated with HIV-infection. Whole-brain cortical mean-field dynamic modeling provides a way to integrate structural and functional imaging outcomes, allowing investigation of microscale brain dynamics. In this study, we adopted the relaxed mean-field dynamic modeling to investigate structural and functional connectivity in 42 HIV-infected subjects before and after 12-week of combination antiretroviral therapy (cART) and compared them with 46 age-matched healthy subjects. Microscale brain dynamics were modeled by a set of parameters including two region-specific microscale brain properties, recurrent connection strengths, and subcortical inputs. We also analyzed the relationship between the model parameters (i.e., the recurrent connection and subcortical inputs) and functional network topological characterizations, including smallworldness, clustering coefficient, and network efficiency. The results show that untreated HIV-infected individuals have disrupted local brain dynamics that in part correlate with network topological measurements. Notably, after 12 weeks of cART, both the microscale brain dynamics and the network topological measurements improved and were closer to those in the healthy brain. This was also associated with improved cognitive performance, suggesting that improvement in local brain dynamics translates into clinical improvement.

Telomeric alterations in the default mode network during the progression of Alzheimer's disease: Selective vulnerability of the precuneus.

AIMS: Although telomere length (TL) and telomere maintenance proteins (shelterins) are markers of cellular senescence and peripheral blood biomarkers of Alzheimer's disease (AD), little information is available on telomeric alterations during the prodromal stage (MCI) of AD. We investigated TL in the default mode network (DMN), which underlies episodic memory deficits in AD, as well as shelterin protein and mRNA levels in the precuneus (PreC). METHODS: Telomere length was evaluated in DMN hubs and visual cortex using quantitative PCR (qPCR). In the PreC, western blotting and NanoString nCounter expression analyses evaluated shelterin protein and mRNA levels, respectively, in cases with an antemortem clinical diagnosis of no cognitive impairment (NCI), MCI and AD. RESULTS: TL was significantly reduced in the PreC in MCI and AD compared to NCI, but stable in frontal, inferior temporal, posterior cingulate and visual cortex. PreC TL correlated significantly with performance on cognitive tests. NCI cases with high vs low Braak scores displayed significantly shorter TL in posterior cingulate and frontal cortex, which correlated significantly with neuritic and diffuse amyloid-ß plaque counts. Shelterin protein levels (TIN2, TRF1, TRF2 and POT1) declined in MCI and AD compared to NCI. The PreC displayed stable expression of shelterins TERF1, TERF2, POT1, RAP1 and TPP1, while TINF2 mRNA significantly increased in AD compared to NCI. CONCLUSIONS: These findings indicate a selective vulnerability to telomere attrition within different nodes of the DMN in prodromal AD and in aged NCI individuals with high Braak scores highlighting a putative role in the pathogenesis of AD.

Finding specificity in structural brain alterations through Bayesian reverse inference.

In the field of neuroimaging reverse inferences can lead us to suppose the involvement of cognitive processes from certain patterns of brain activity. However, the same reasoning holds if we substitute "brain activity" with "brain alteration" and "cognitive process" with "brain disorder." The fact that different brain disorders exhibit a high degree of overlap in their patterns of structural alterations makes forward inference-based analyses less suitable for identifying brain areas whose alteration is specific to a certain pathology. In the forward inference-based analyses, in fact, it is impossible to distinguish between areas that are altered by the majority of brain disorders and areas that are specifically affected by certain diseases. To address this issue and allow the identification of highly pathology-specific altered areas we used the Bayes' factor technique, which was employed, as a proof of concept, on voxel-based morphometry data of schizophrenia and Alzheimer's disease. This technique allows to calculate the ratio between the likelihoods of two alternative hypotheses (in our case, that the alteration of the voxel is specific for the brain disorder under scrutiny or that the alteration is not specific). We then performed temporal simulations of the alterations' spread associated with different pathologies. The Bayes' factor values calculated on these simulated data were able to reveal that the areas, which are more specific to a certain disease, are also the ones to be early altered. This study puts forward a new analytical instrument capable of innovating the methodological approach to the investigation of brain pathology.

A meta-analytic approach to mapping co-occurrent grey matter volume increases and decreases in psychiatric disorders.

Numerous studies have investigated grey matter (GM) volume changes in diverse patient groups. Reports of disorder-related GM reductions are common in such work, but many studies also report evidence for GM volume increases in patients. It is unclear whether these GM increases and decreases are independent or related in some way. Here, we address this question using a novel meta-analytic network mapping approach. We used a coordinate-based meta-analysis of 64 voxel-based morphometry studies of psychiatric disorders to calculate the probability of finding a GM increase or decrease in one region given an observed change in the opposite direction in another region. Estimating this co-occurrence probability for every pair of brain regions allowed us to build a network of concurrent GM changes of opposing polarity. Our analysis revealed that disorder-related GM increases and decreases are not independent; instead, a GM change in one area is often statistically related to a change of opposite polarity in other areas, highlighting distributed yet coordinated changes in GM volume as a function of brain pathology. Most regions showing GM changes linked to an opposite change in a distal area were located in salience, executive-control and default mode networks, as well as the thalamus and basal ganglia. Moreover, pairs of regions showing coupled changes of opposite polarity were more likely to belong to different canonical networks than to the same one. Our results suggest that regional GM alterations in psychiatric disorders are often accompanied by opposing changes in distal regions that belong to distinct functional networks.

Latent Clinical-Anatomical Dimensions of Schizophrenia.

Widespread structural brain abnormalities have been consistently reported in schizophrenia, but their relation to the heterogeneous clinical manifestations remains unknown. In particular, it is unclear whether anatomical abnormalities in discrete regions give rise to discrete symptoms or whether distributed abnormalities give rise to the broad clinical profile associated with schizophrenia. Here, we apply a multivariate data-driven approach to investigate covariance patterns between multiple-symptom domains and distributed brain abnormalities in schizophrenia. Structural magnetic resonance imaging and clinical data were derived from one discovery sample (133 patients and 113 controls) and one independent validation sample (108 patients and 69 controls). Disease-related voxel-wise brain abnormalities were estimated using deformation-based morphometry. Partial least-squares analysis was used to comprehensively map clinical, neuropsychological, and demographic data onto distributed deformation in a single multivariate model. The analysis identified 3 latent clinical-anatomical dimensions that collectively accounted for 55% of the covariance between clinical data and brain deformation. The first latent clinical-anatomical dimension was replicated in an independent sample, encompassing cognitive impairments, negative symptom severity, and brain abnormalities within the default mode and visual networks. This cognitive-negative dimension was associated with low socioeconomic status and was represented across multiple races. Altogether, we identified a continuous cognitive-negative dimension of schizophrenia, centered on 2 intrinsic networks. By simultaneously taking into account both clinical manifestations and neuroanatomical abnormalities, the present results open new avenues for multi-omic stratification and biotyping of individuals with schizophrenia.

Asymmetric tumor-related alterations of network-specific intrinsic functional connectivity in glioma patients.

Resting-state functional MRI (rs-fMRI) allows mapping temporally coherent brain networks, and intra- and inter-network alterations have been described in different diseases. This prospective study investigated hemispheric resting-state functional connectivity (RSFC) differences in the default-mode network (DMN) and fronto-parietal network (FPN) between patients with left- and right-hemispheric gliomas (LH PAT, RH PAT), addressing asymmetry effects the tumor might have on network-specific intrinsic functional connectivity under consideration of the prognostically relevant isocitrate-dehydrogenase (IDH) mutation status. Twenty-seven patients (16 LH PAT, 12 IDH-wildtype) and 27 healthy controls underwent anatomical and rs-fMRI as well as neuropsychological assessment. Independent component analyses were performed to identify the DMN and FPN. Hemispheric DMN- and FPN-RSFC were computed, compared across groups, and correlated with cognitive performance. Patient groups did not differ in tumor volume, grade or location. RH PAT showed higher contra-tumoral DMN-RSFC than controls and LH PAT. With regard to the FPN, contra-tumoral RSFC was increased in both patient groups as compared to controls. Higher contra-tumoral RSFC was associated with worse cognitive performance in patients, which, however, seemed to apply mainly to IDH-wildtype patients. The benefit of RSFC alterations for cognitive performance varied depending on the affected hemisphere, cognitive demand, and seemed to be altered by IDH-mutation status. At the time of study initiation, a clinical trial registration was not mandatory at our faculty, but it can be applied for if requested.

Cumulative Risk on Oxytocin-Pathway Genes Impairs Default Mode Network Connectivity in Trauma-Exposed Youth.

Background: Although the default mode network (DMN) is a core network essential for brain functioning, little is known about its developmental trajectory, particularly on factors associated with its coherence into a functional network. In light of adult studies indicating DMN's susceptibility to stress-related conditions, we examined links between variability on oxytocin-pathway genes and DMN connectivity in youth exposed to chronic war-related trauma Methods: Following a cohort of war-exposed children from early childhood, we imaged the brains of 74 preadolescents (age 11-13 years; 39 war-exposed) during rest using magnetoencephalography (MEG). A cumulative risk index on oxytocin-pathway genes was constructed by combining single nucleotide polymorphisms on five genes previously linked with social deficits and psychopathology; OXTR rs1042778, OXTR rs2254298, OXTRrs53576, CD38 rs3796863, and AVPR1A RS3. Avoidant response to trauma reminders in early childhood and anxiety disorders in late childhood were assessed as predictors of disruptions to DMN theta connectivity. Results: Higher vulnerability on oxytocin-pathway genes predicted greater disruptions to DMN theta connectivity. Avoidant symptoms in early childhood and generalized anxiety disorder in later childhood were related to impaired DMN connectivity. In combination, stress exposure, oxytocin-pathway genes, and stress-related symptoms explained 24.6% of the variance in DMN connectivity, highlighting the significant effect of stress on the maturing brain. Conclusions: Findings are the first to link the oxytocin system and maturation of the DMN, a core system sustaining autobiographical memories, alteration of intrinsic and extrinsic attention, mentalization, and sense of self. Results suggest that oxytocin may buffer the effects of chronic early stress on the DMN, particularly theta rhythms that typify the developing brain.

Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors.

In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1-year follow-up was assessed in 30 individuals with a schizophrenia-spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting-state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1-year follow-up varied markedly among individuals (interquartile range: 55%). Dynamic resting-state connectivity measured within the default-mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow-up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (<70%). Worsening negative symptoms at 1-year follow-up were predicted by hyper-connectivity and hypo-dynamism within the default-mode network at baseline assessment, while hypo-connectivity and hyper-dynamism predicted worsening positive symptoms. Given the modest sample size investigated, we recommend giving precedence to the relative ranking of the predictors investigated in this study, rather than the prediction accuracy estimates.

Alterations and test-retest reliability of functional connectivity network measures in cerebral small vessel disease.

While structural network analysis consolidated the hypothesis of cerebral small vessel disease (SVD) being a disconnection syndrome, little is known about functional changes on the level of brain networks. In patients with genetically defined SVD (CADASIL, n = 41) and sporadic SVD (n = 46), we independently tested the hypothesis that functional networks change with SVD burden and mediate the effect of disease burden on cognitive performance, in particular slowing of processing speed. We further determined test-retest reliability of functional network measures in sporadic SVD patients participating in a high-frequency (monthly) serial imaging study (RUN DMC-InTENse, median: 8 MRIs per participant). Functional networks for the whole brain and major subsystems (i.e., default mode network, DMN; fronto-parietal task control network, FPCN; visual network, VN; hand somatosensory-motor network, HSMN) were constructed based on resting-state multi-band functional MRI. In CADASIL, global efficiency (a graph metric capturing network integration) of the DMN was lower in patients with high disease burden (standardized beta = -.44; p [corrected] = .035) and mediated the negative effect of disease burden on processing speed (indirect path: std. beta = -.20, p = .047; direct path: std. beta = -.19, p = .25; total effect: std. beta = -.39, p = .02). The corresponding analyses in sporadic SVD showed no effect. Intraclass correlations in the high-frequency serial MRI dataset of the sporadic SVD patients revealed poor test-retest reliability and analysis of individual variability suggested an influence of age, but not disease burden, on global efficiency. In conclusion, our results suggest that changes in functional connectivity networks mediate the effect of SVD-related brain damage on cognitive deficits. However, limited reliability of functional network measures, possibly due to age-related comorbidities, impedes the analysis in elderly SVD patients.