Normative Magnetic Resonance Imaging Data Increase the Sensitivity to Brain Volume Abnormalities in the Classification of Fetal Alcohol Spectrum Disorder.

OBJECTIVE: To evaluate the use of a large magnetic resonance imaging (MRI) normative dataset to quantify structural brain anomalies that may improve diagnostic sensitivity for atypical brain volume in youth with fetal alcohol spectrum disorder (FASD). STUDY DESIGN: Participants included 48 children with prenatal alcohol exposure (PAE) and 43 controls, ages 8-17 years, from the longitudinal Collaborative Initiative on FASD s. Recently published lifespan brain charts were used to quantify participants' (per)centile for brain volumes (cortical and subcortical gray matter and cortical white matter), providing an index of (dis)similarity to typically developing individuals of the same age and sex. RESULTS: Participants with PAE demonstrated lower mean centile scores compared with controls. Participants with PAE and scores ≤ 10th centile on at least 1 brain volume metric demonstrated significantly lower performance on measures of intellectual function and aspects of executive functioning compared with participants with PAE and "typical" volumes (>10th centile). Brain volume centiles explained a greater amount of variance in IQ and improved sensitivity to brain volume anomalies in FASD compared with the most commonly used diagnostic criterion of occipitofrontal circumference (OFC) ≤ 10th. CONCLUSION: Age- and sex-adjusted brain volumes based on a large normative dataset may be useful predictors of functional outcomes and may identify a greater number of individuals with FASD than the currently used criterion of OFC.

Relating white matter microstructure in theoretically defined addiction networks to relapse in alcohol use disorder.

Theoretical models group maladaptive behaviors in addiction into neurocognitive domains such as incentive salience (IS), negative emotionality (NE), and executive functioning (EF). Alterations in these domains lead to relapse in alcohol use disorder (AUD). We examine whether microstructural measures in the white matter pathways supporting these domains are associated with relapse in AUD. Diffusion kurtosis imaging data were collected from 53 individuals with AUD during early abstinence. We used probabilistic tractography to delineate the fornix (IS), uncinate fasciculus (NE), and anterior thalamic radiation (EF) in each participant and extracted mean fractional anisotropy (FA) and kurtosis fractional anisotropy (KFA) within each tract. Binary (abstained vs. relapsed) and continuous (number of days abstinent) relapse measures were collected over a 4-month period. Across tracts, anisotropy measures were typically (i) lower in those that relapsed during the follow-up period and (ii) positively associated with the duration of sustained abstinence during the follow-up period. However, only KFA in the right fornix reached significance in our sample. The association between microstructural measures in these fiber tracts and treatment outcome in a small sample highlights the potential utility of the three-factor model of addiction and the role of white matter alterations in AUD.

Reliability and clinical utility of spatially constrained estimates of intrinsic functional networks from very short fMRI scans.

Resting-state functional network connectivity (rsFNC) has shown utility for identifying characteristic functional brain patterns in individuals with psychiatric and mood disorders, providing a promising avenue for biomarker development. However, several factors have precluded widespread clinical adoption of rsFNC diagnostics, namely a lack of standardized approaches for capturing comparable and reproducible imaging markers across individuals, as well as the disagreement on the amount of data required to robustly detect intrinsic connectivity networks (ICNs) and diagnostically relevant patterns of rsFNC at the individual subject level. Recently, spatially constrained independent component analysis (scICA) has been proposed as an automated method for extracting ICNs standardized to a chosen network template while still preserving individual variation. Leveraging the scICA methodology, which solves the former challenge of standardized neuroimaging markers, we investigate the latter challenge of identifying a minimally sufficient data length for clinical applications of resting-state fMRI (rsfMRI). Using a dataset containing rsfMRI scans of individuals with schizophrenia and controls (M = 310) as well as simulated rsfMRI, we evaluated the robustness of ICN and rsFNC estimates at both the subject- and group-level, as well as the performance of diagnostic classification, with respect to the length of the rsfMRI time course. We found individual estimates of ICNs and rsFNC from the full-length (5 min) reference time course were sufficiently approximated with just 3-3.5 min of data (r = 0.85, 0.88, respectively), and significant differences in group-average rsFNC could be sufficiently approximated with even less data, just 2 min (r = 0.86). These results from the shorter clinical data were largely consistent with the results from validation experiments using longer time series from both simulated (30 min) and real-world (14 min) datasets, in which estimates of subject-level FNC were reliably estimated with 3-5 min of data. Moreover, in the real-world data we found rsFNC and ICN estimates generated across the full range of data lengths (0.5-14 min) more reliably matched those generated from the first 5 min of scan time than those generated from the last 5 min, suggesting increased influence of "late scan" noise factors such as fatigue or drowsiness may limit the reliability of FNC from data collected after 10+ min of scan time, further supporting the notion of shorter scans. Lastly, a diagnostic classification model trained on just 2 min of data retained 97%-98% classification accuracy relative to that of the full-length reference model. Our results suggest that, when decomposed with scICA, rsfMRI scans of just 2-5 min show good clinical utility without significant loss of individual FNC information of longer scan lengths.

Patterns of experience, expression, and physiology of stress relate to depressive symptoms and self-injurious thoughts and behaviors in adolescents: a person-centered approach.

BACKGROUND: Preliminary evidence shows that discordance in stress experience, expression, and physiology (EEP) in adolescents is linked to depression, suicidal ideation (SI), non-suicidal self-injury (NSSI), and brain functioning. This study employs person-centered analysis to probe the relationship between stress responses, psychopathology, and neural patterns in female adolescents who are oversampled for engagement in NSSI. METHODS: Adolescent females (N = 109, ages 12-17) underwent a social stress test from which self-report measures of stress experience, observer ratings of stress expression, and physiological metrics of stress (via salivary cortisol) were obtained. Multi-trajectory modeling was employed to identify concordant and discordant stress EEP groups. Depressive symptoms, SI and attempt, NSSI engagement, frontal and limbic activation to emotional stimuli, and resting state fronto-limbic connectivity were examined in the EEP groups derived from the multi-trajectory models. RESULTS: Four groups were identified, three of which demonstrated relatively concordant EEP and one which demonstrated discordant EEP (High Experience-High Expression-Low Physiology). Further, replicating past research, the High Experience-High Expression-Low Physiology discordant group exhibited higher depressive symptoms, SI, suicide attempt, and NSSI episodes (only for sensitivity analyses based on past year) relative to other EEP groups. No significant group differences in brain functioning emerged. CONCLUSION: Results indicate that within-person, multi-level patterns in stress responding capture risk for dysfunction including depression and self-injurious thoughts and behaviors. Further interrogating of system-level stress functioning may better inform assessment and intervention efforts.

The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks.

We demonstrate a data-driven approach for calculating a "causal connectome" of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.

Nonlinear functional network connectivity in resting functional magnetic resonance imaging data.

In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (NMI) that calculates the nonlinear relationship between different brain regions. We demonstrate our proposed approach using simulated data and then apply it to a dataset previously studied by Damaraju et al. This resting-state fMRI data included 151 schizophrenia patients and 163 age- and gender-matched healthy controls. We first decomposed these data using group independent component analysis (ICA) and yielded 47 functionally relevant intrinsic connectivity networks. Our analysis showed a modularized nonlinear relationship among brain functional networks that was particularly noticeable in the sensory and visual cortex. Interestingly, the modularity appears both meaningful and distinct from that revealed by the linear approach. Group analysis identified significant differences in explicitly nonlinear functional network connectivity (FNC) between schizophrenia patients and healthy controls, particularly in the visual cortex, with controls showing more nonlinearity (i.e., higher normalized mutual information between time courses with linear relationships removed) in most cases. Certain domains, including subcortical and auditory, showed relatively less nonlinear FNC (i.e., lower normalized mutual information), whereas links between the visual and other domains showed evidence of substantial nonlinear and modular properties. Overall, these results suggest that quantifying nonlinear dependencies of functional connectivity may provide a complementary and potentially important tool for studying brain function by exposing relevant variation that is typically ignored. Beyond this, we propose a method that captures both linear and nonlinear effects in a "boosted" approach. This method increases the sensitivity to group differences compared to the standard linear approach, at the cost of being unable to separate linear and nonlinear effects.

A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium.

Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.

Tralesinidase Alfa Enzyme Replacement Therapy Prevents Disease Manifestations in a Canine Model of Mucopolysaccharidosis Type IIIB.

Mucopolysaccharidosis type IIIB (MPS IIIB; Sanfilippo syndrome B; OMIM #252920) is a lethal, pediatric, neuropathic, autosomal recessive, and lysosomal storage disease with no approved therapy. Patients are deficient in the activity of N-acetyl-alpha-glucosaminidase (NAGLU; EC 3.2.150), necessary for normal lysosomal degradation of the glycosaminoglycan heparan sulfate (HS). Tralesinidase alfa (TA), a fusion protein comprised of recombinant human NAGLU and a modified human insulin-like growth factor 2, is in development as an enzyme replacement therapy that is administered via intracerebroventricular (ICV) infusion, thus circumventing the blood brain barrier. Previous studies have confirmed ICV infusion results in widespread distribution of TA throughout the brains of mice and nonhuman primates. We assessed the long-term tolerability, pharmacology, and clinical efficacy of TA in a canine model of MPS IIIB over a 20-month study. Long-term administration of TA was well tolerated as compared with administration of vehicle. TA was widely distributed across brain regions, which was confirmed in a follow-up 8-week pharmacokinetic/pharmacodynamic study. MPS IIIB dogs treated for up to 20 months had near-normal levels of HS and nonreducing ends of HS in cerebrospinal fluid and central nervous system (CNS) tissues. TA-treated MPS IIIB dogs performed better on cognitive tests and had improved CNS pathology and decreased cerebellar volume loss relative to vehicle-treated MPS IIIB dogs. These findings demonstrate the ability of TA to prevent or limit the biochemical, pathologic, and cognitive manifestations of canine MPS IIIB disease, thus providing support of its potential long-term tolerability and efficacy in MPS IIIB subjects. SIGNIFICANCE STATEMENT: This work illustrates the efficacy and tolerability of tralesinidase alfa as a potential therapeutic for patients with mucopolysaccharidosis type IIIB (MPS IIIB) by documenting that administration to the central nervous system of MPS IIIB dogs prevents the accumulation of disease-associated glycosaminoglycans in lysosomes, hepatomegaly, cerebellar atrophy, and cognitive decline.

Quantitative brain MRI morphology in severe and attenuated forms of mucopolysaccharidosis type I.

OBJECTIVE: To assess our hypothesis that brain macrostructure is different in individuals with mucopolysaccharidosis type I (MPS I) and healthy controls (HC), we conducted a comprehensive multicenter study using a uniform quantitative magnetic resonance imaging (qMRI) protocol, with analyses that account for the effects of disease phenotype, age, and cognition. METHODS: Brain MRIs in 23 individuals with attenuated (MPS IA) and 38 with severe MPS I (MPS IH), aged 4-25 years, enrolled under the study protocol NCT01870375, were compared to 98 healthy controls. RESULTS: Cortical and subcortical gray matter, white matter, corpus callosum, ventricular and choroid plexus volumes in MPS I significantly differed from HC. Thicker cortex, lower white matter and corpus callosum volumes were already present at the youngest MPS I participants aged 4-5 years. Age-related differences were observed in both MPS I groups, but most markedly in MPS IH, particularly in cortical gray matter metrics. IQ scores were inversely associated with ventricular volume in both MPS I groups and were positively associated with cortical thickness only in MPS IA. CONCLUSIONS: Quantitatively-derived MRI measures distinguished MPS I participants from HC as well as severe from attenuated forms. Age-related neurodevelopmental trajectories in both MPS I forms differed from HC. The extent to which brain structure is altered by disease, potentially spared by treatment, and how it relates to neurocognitive dysfunction needs further exploration.

Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders : Evidence through univariate and multivariate mega-analysis including 6420 participants from the ENIGMA MDD working group.

Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced effects were found for associations between obesity and lower temporo-frontal cortical thickness (maximum Cohen´s d (left fusiform gyrus) = -0.33). The observed regional distribution and effect size of cortical thickness reductions in obesity revealed considerable similarities with corresponding patterns of lower cortical thickness in previously published studies of neuropsychiatric disorders. A higher polygenic risk score for obesity significantly correlated with lower occipital surface area. In addition, a significant age-by-obesity interaction on cortical thickness emerged driven by lower thickness in older participants. Our findings suggest a neurobiological interaction between obesity and brain structure under physiological and pathological brain conditions.

Resting State Hypoconnectivity of Reward Networks in Binge Eating Disorder.

The clinical presentation of binge eating disorder (BED) and data emerging from task-based functional neuroimaging research suggests that this disorder may be associated with alterations in reward processing. However, there is a dearth of research investigating the functional organization of brain networks that mediate reward in BED. To address this gap, 27 adults with BED and 21 weight-matched healthy controls (WMC) completed a multimodel assessment consisting of a resting functional magnetic resonance imaging scan, behavioral tasks measuring reward-based decision-making (i.e., delay discounting and reversal learning), and self-report assessing clinical symptoms. A seed-based approach was employed to examine the resting state functional connectivity (rsFC) of the striatum (nucleus accumbens [NAcc] and ventral and dorsal caudate), a collection of regions implicated in reward processing. Compared with WMC, the BED group exhibited lower rsFC of striatal seeds, with frontal regions mediating executive functioning (e.g., superior frontal gyrus [SFG]) and posterior, parietal, and temporal regions implicated in emotional processing. Lower NAcc-SFG rsFC was associated with more difficulties with reversal learning and binge eating frequency in the BED group. Results suggest that hypoconnectivity of striatal networks that integrate self-regulation and reward processing may promote the clinical phenomenology of BED. Interventions for BED may benefit from targeting these circuit-based disturbances.

The psychosis human connectome project: An overview.

Investigations within the Human Connectome Project have expanded to include studies focusing on brain disorders. This paper describes one of the investigations focused on psychotic psychopathology: The psychosis Human Connectome Project (P-HCP). The data collected as part of this project were multimodal and derived from clinical assessments of psychopathology, cognitive assessments, instrument-based motor assessments, blood specimens, and magnetic resonance imaging (MRI) data. The dataset will be made publicly available through the NIMH Data Archive. In this report we provide specific information on how the sample of participants was obtained and characterized and describe the experimental tasks and procedures used to probe neural functions involved in psychotic disorders that may also mark genetic liability for psychotic psychopathology. Our goal in this paper is to outline the data acquisition process so that researchers intending to use these publicly available data can plan their analyses. MRI data described in this paper are limited to data acquired at 3 Tesla. A companion paper describes the study's 7 Tesla image acquisition protocol in detail, which is focused on visual perceptual functions in psychotic psychopathology.

Multimodal assessment of sustained threat in adolescents with nonsuicidal self-injury.

Nonsuicidal self-injury (NSSI) is a common but poorly understood phenomenon in adolescents. This study examined the Sustained Threat domain in female adolescents with a continuum of NSSI severity (N = 142). Across NSSI lifetime frequency and NSSI severity groups (No + Mild NSSI, Moderate NSSI, Severe NSSI), we examined physiological, self-reported and observed stress during the Trier Social Stress Test; amygdala volume; amygdala responses to threat stimuli; and resting-state functional connectivity (RSFC) between amygdala and medial prefrontal cortex (mPFC). Severe NSSI showed a blunted pattern of cortisol response, despite elevated reported and observed stress during TSST. Severe NSSI showed lower amygdala-mPFC RSFC; follow-up analyses suggested that this was more pronounced in those with a history of suicide attempt for both moderate and severe NSSI. Moderate NSSI showed elevated right amygdala activation to threat; multiple regressions showed that, when considered together with low amygdala-mPFC RSFC, higher right but lower left amygdala activation predicted NSSI severity. Patterns of interrelationships among Sustained Threat measures varied substantially across NSSI severity groups, and further by suicide attempt history. Study limitations include the cross-sectional design, missing data, and sampling biases. Our findings highlight the value of multilevel approaches in understanding the complexity of neurobiological mechanisms in adolescent NSSI.

Covarying structural alterations in laterality of the temporal lobe in schizophrenia: A case for source-based laterality.

The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation. To address this limitation, source-based laterality (SBL) leverages an independent component analysis for the identification of laterality-specific alterations, identifying covarying components between hemispheres across subjects. SBL is successfully implemented with simulated data with inherent differences in laterality. SBL is then compared with a voxel-wise analysis utilizing structural data from a sample of patients with schizophrenia and controls without schizophrenia. SBL group comparisons identified three distinct temporal regions and one cerebellar region with significantly altered laterality in patients with schizophrenia relative to controls. Previous work highlights reductions in laterality (ie, reduced left gray matter volume) in patients with schizophrenia compared with controls without schizophrenia. Results from this pilot SBL project are the first, to our knowledge, to identify covarying laterality differences within discrete temporal brain regions. The authors argue SBL provides a unique focus to detect covarying laterality differences in patients with schizophrenia, facilitating the discovery of laterality aspects undetected in previous work.

Para-limbic Structural Abnormalities Are Associated With Internalizing Symptoms in Children With Prenatal Alcohol Exposure.

BACKGROUND: Prenatal alcohol exposure (PAE) is associated with a variety of structural abnormalities in the brain, including several within the para-limbic system. Children with PAE have higher rates of internalizing disorders, including depression and anxiety, which may be related to underlying limbic system anomalies. METHODS: Children aged 8 to 16 with PAE (n = 41) or without PAE (n = 36) underwent an magnetic resonance imaging of the brain and parents completed behavioral questionnaires about their children. Semi-automated procedures (FreeSurfer) were used to derive para-limbic volumes from T1-weighted anatomical images. RESULTS: There were significant group differences (PAE vs. nonexposed controls) in the caudate, hippocampus, and the putamen; children with PAE had smaller volumes in these regions even after controlling for total intracranial volume. A trend-level association was seen between caudate volume and internalizing symptoms in children with PAE; smaller caudate volumes (presumably reflecting less optimal neurodevelopment) were associated with higher levels of anxiety and depression symptoms in these children. CONCLUSIONS: Caudate structure may be disproportionately affected by PAE and may be associated with the later development of internalizing symptoms in those affected by PAE.

A framework for linking resting-state chronnectome/genome features in schizophrenia: A pilot study.

Multimodal, imaging-genomics techniques offer a platform for understanding genetic influences on brain abnormalities in psychiatric disorders. Such approaches utilize the information available from both imaging and genomics data and identify their association. Particularly for complex disorders such as schizophrenia, the relationship between imaging and genomic features may be better understood by incorporating additional information provided by advanced multimodal modeling. In this study, we propose a novel framework to combine features corresponding to functional magnetic resonance imaging (functional) and single nucleotide polymorphism (SNP) data from 61 schizophrenia (SZ) patients and 87 healthy controls (HC). In particular, the features for the functional and genetic modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) features and the SNP data, respectively. The dFNC features are estimated from component time-courses, obtained using group independent component analysis (ICA), by computing sliding-window functional network connectivity, and then estimating subject specific states from this dFNC data using a k-means clustering approach. For each subject, both the functional (dFNC states) and SNP data are selected as features for a parallel ICA (pICA) based imaging-genomic framework. This analysis identified a significant association between a SNP component (defined by large clusters of functionally related SNPs statistically correlated with phenotype components) and time-varying or dFNC component (defined by clusters of related connectivity links among distant brain regions distributed across discrete dynamic states, and statistically correlated with genomic components) in schizophrenia. Importantly, the polygenetic risk score (PRS) for SZ (computed as a linearly weighted sum of the genotype profiles with weights derived from the odds ratios of the psychiatric genomics consortium (PGC)) was negatively correlated with the significant dFNC component, which were mostly present within a state that exhibited a lower occupancy rate in individuals with SZ compared with HC, hence identifying a potential dFNC imaging biomarker for schizophrenia. Taken together, the current findings provide preliminary evidence for a link between dFNC measures and genetic risk, suggesting the application of dFNC patterns as biomarkers in imaging genetic association study.

Neurite orientation dispersion and density imaging quantifies corticospinal tract microstructural organization in children with unilateral cerebral palsy.

Children with unilateral cerebral palsy (UCP) due to early brain injury exhibit disrupted connectivity of corticospinal tracts (CSTs), which can be quantified using diffusion-weighted magnetic resonance imaging (DWI). Diffusion tensor imaging (DTI) is commonly used to quantify white matter organization, however, this model lacks the biological specificity to accurately describe underlying microstructural properties. Newer approaches, such as neurite orientation dispersion and density imaging (NODDI), may provide more biologically accurate information regarding CST microstructure. In this study, we directly compared metrics of CST microstructure using NODDI and DTI models to characterize the microstructural organization of corticospinal pathways. Twenty participants with UCP participating in a neuromodulation/rehabilitation intervention underwent imaging including multi-shell DWI; 10 participants' datasets were adequately completed for neuroimaging analysis. Task fMRI-guided probabilistic tractography from motor cortex to brainstem was performed at baseline and follow-up to reconstruct the CSTs. Diffusion metrics were compared between hemispheres at baseline, and between baseline and follow-up to test for intervention effects. Correlation analyses were used to compare baseline metrics to changes in hand function following the intervention. DTI results showed that mean fractional anisotropy in lesioned and nonlesioned CSTs did not significantly differ, but mean, axial, and radial diffusivity were greater in the lesioned CST. For NODDI, intracellular volume fraction (ICVF) and orientation dispersion index (ODI) were lower in the lesioned CST. Unimanual function was strongly correlated with ICVF, but not FA. NODDI may reveal distinct properties of CST microstructure that are linked to motor function, indicating their potential in characterizing brain structure and development.

The spatial chronnectome reveals a dynamic interplay between functional segregation and integration.

The brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales, including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns change in fundamental and recurring ways over time. Most literature assumes fixed spatial nodes/networks, ignoring the possibility that spatial nodes/networks may vary in time. Here, we introduce an approach to calculate a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variations of networks coupling at the voxel level, and identify a novel set of spatially dynamic features. Results reveal transient spatially fluid interactions between intra- and internetwork relationships in which brain networks transiently merge and separate, emphasizing dynamic segregation and integration. Brain networks also exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies that assumed static networks. Moreover, we show anticorrelative connections to brain networks are transient as opposed to constant across the entire scan. Preliminary assessments using a multi-site dataset reveal the ability of the approach to obtain new information and nuanced alterations that remain undetected during static analysis. Patients with schizophrenia (SZ) display transient decreases in voxel-wise network coupling within visual and auditory networks, and higher intradomain coupling variability. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks which are transient at the voxel level, and the identification of mechanisms for within- and between-subject spatial variability.

Parallel group ICA+ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia.

There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called "parallel group ICA+ICA" that incorporates temporal fMRI information from group independent component analysis (GICA) into a parallel independent component analysis (ICA) framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g., structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate intermodality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities, and this linkage can be replicated in an independent cohort. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.

Spatial dynamics within and between brain functional domains: A hierarchical approach to study time-varying brain function.

The analysis of time-varying activity and connectivity patterns (i.e., the chronnectome) using resting-state magnetic resonance imaging has become an important part of ongoing neuroscience discussions. The majority of previous work has focused on variations of temporal coupling among fixed spatial nodes or transition of the dominant activity/connectivity pattern over time. Here, we introduce an approach to capture spatial dynamics within functional domains (FDs), as well as temporal dynamics within and between FDs. The approach models the brain as a hierarchical functional architecture with different levels of granularity, where lower levels have higher functional homogeneity and less dynamic behavior and higher levels have less homogeneity and more dynamic behavior. First, a high-order spatial independent component analysis is used to approximate functional units. A functional unit is a pattern of regions with very similar functional activity over time. Next, functional units are used to construct FDs. Finally, functional modules (FMs) are calculated from FDs, providing an overall view of brain dynamics. Results highlight the spatial fluidity within FDs, including a broad spectrum of changes in regional associations, from strong coupling to complete decoupling. Moreover, FMs capture the dynamic interplay between FDs. Patients with schizophrenia show transient reductions in functional activity and state connectivity across several FDs, particularly the subcortical domain. Activity and connectivity differences convey unique information in many cases (e.g., the default mode) highlighting their complementarity information. The proposed hierarchical model to capture FD spatiotemporal variations provides new insight into the macroscale chronnectome and identifies changes hidden from existing approaches.