A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

Abnormal dynamic functional connectivity and topological properties of cerebellar network in male obstructive sleep apnea

PURPOSE

To investigate dynamic functional connectivity (dFC) within the cerebellar-whole brain network and dynamic topological properties of the cerebellar network in obstructive sleep apnea (OSA) patients.

METHODS

Sixty male patients and 60 male healthy controls were included. The sliding window method examined the fluctuations in cerebellum-whole brain dFC and connection strength in OSA. Furthermore, graph theory metrics evaluated the dynamic topological properties of the cerebellar network. Additionally, hidden Markov modeling validated the robustness of the dFC. The correlations between the abovementioned measures and clinical assessments were assessed.

RESULTS

Two dynamic network states were characterized. State 2 exhibited a heightened frequency, longer fractional occupancy, and greater mean dwell time in OSA. The cerebellar networks and cerebrocerebellar dFC alterations were mainly located in the default mode network, frontoparietal network, somatomotor network, right cerebellar CrusI/II, and other networks. Global properties indicated aberrant cerebellar topology in OSA. Dynamic properties were correlated with clinical indicators primarily on emotion, cognition, and sleep.

CONCLUSION

Abnormal dFC in male OSA may indicate an imbalance between the integration and segregation of brain networks, concurrent with global topological alterations. Abnormal default mode network interactions with high-order and low-level cognitive networks, disrupting their coordination, may impair the regulation of cognitive, emotional, and sleep functions in OSA.

White matter alterations and their associations with biomarkers and behavior in subjective cognitive decline individuals: a fixel-based analysis

BACKGROUND

Subjective cognitive decline (SCD) is an early stage of dementia linked to Alzheimer's disease pathology. White matter changes were found in SCD using diffusion tensor imaging, but there are known limitations in voxel-wise tensor-based methods. Fixel-based analysis (FBA) can help understand changes in white matter fibers and how they relate to neurodegenerative proteins and multidomain behavior data in individuals with SCD.

METHODS

Healthy adults with normal cognition were recruited in the Northeastern Taiwan Community Medicine Research Cohort in 2018-2022 and divided into SCD and normal control (NC). Participants underwent evaluations to assess cognitive abilities, mental states, physical activity levels, and susceptibility to fatigue. Neurodegenerative proteins were measured using an immunomagnetic reduction technique. Multi-shell diffusion MRI data were collected and analyzed using whole-brain FBA, comparing results between groups and correlating them with multidomain assessments.

RESULTS

The final enrollment included 33 SCD and 46 NC participants, with no significant differences in age, sex, or education between the groups. SCD had a greater fiber-bundle cross-section than NC (pFWE < 0.05) at bilateral frontal superior longitudinal fasciculus II (SLFII). These white matter changes correlate negatively with plasma Aβ42 level (r = -0.38, p = 0.01) and positively with the AD8 score for subjective cognitive complaints (r = 0.42, p = 0.004) and the Hamilton Anxiety Rating Scale score for the degree of anxiety (Ham-A, r = 0.35, p = 0.019). The dimensional analysis of FBA metrics and blood biomarkers found positive correlations of plasma neurofilament light chain with fiber density at the splenium of corpus callosum (pFWE < 0.05) and with fiber-bundle cross-section at the right thalamus (pFWE < 0.05). Further examination of how SCD grouping interacts between the correlations of FBA metrics and multidomain assessments showed interactions between the fiber density at the corpus callosum with letter-number sequencing cognitive score (pFWE < 0.01) and with fatigue to leisure activities (pFWE < 0.05).

CONCLUSION

Based on FBA, our investigation suggests white matter structural alterations in SCD. The enlargement of SLFII's fiber cross-section is linked to plasma Aβ42 and neuropsychiatric symptoms, which suggests potential early axonal dystrophy associated with Alzheimer's pathology in SCD. The splenium of the corpus callosum is also a critical region of axonal degeneration and cognitive alteration for SCD.

Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder

Background

Resting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used extensively to study brain function in psychiatric disorders, yielding insights into brain organization. However, the high dimensionality of the rs-fMRI data presents significant challenges for data analysis. Variational autoencoders (VAEs), a type of neural network, have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity (rsFC) patterns, thereby addressing the complex nonlinear structure of rs-fMRI data. Despite these advances, interpreting these latent representations remains a challenge. This paper aims to address this gap by developing explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD).

Methods

One-thousand one hundred and fifty participants (601 healthy controls [HC] and 549 patients with ASD) were included in the analysis. RsFC correlation matrices were extracted from the preprocessed rs-fMRI data using the Power atlas, which includes 264 regions of interest (ROIs). Then VAEs were trained in an unsupervised manner. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures.

Results

We quantified the latent contribution scores for both the ASD and HC groups at the network level. We found that both ASD and HC groups share the top network connectivitives contributing to all estimated latent components. For example, latent 0 was driven by rsFC within ventral attention network (VAN) in both the ASD and HC. However, we found significant differences in the latent contribution scores between the ASD and HC groups within the VAN for latent 0 and the sensory/somatomotor network for latent 2.

Conclusion

This study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC feature as the estimated latent representation changes, enabling an explainable deep learning model that better understands the underlying neural mechanisms of ASD.

Brain signaling becomes less integrated and more segregated with age

The integration-segregation framework is a popular first step to understand brain dynamics because it simplifies brain dynamics into two states based on global vs. local signaling patterns. However, there is no consensus for how to best define what the two states look like. Here, we map integration and segregation to order and disorder states from the Ising model in physics to calculate state probabilities, P int and P seg , from functional MRI data. We find that integration/segregation decreases/increases with age across three databases, and changes are consistent with weakened connection strength among regions rather than topological connectivity based on structural and diffusion MRI data.

Using a deep generation network reveals neuroanatomical specificity in hemispheres

Asymmetry is an important property of brain organization, but its nature is still poorly understood. Capturing the neuroanatomical components specific to each hemisphere facilitates the understanding of the establishment of brain asymmetry. Since deep generative networks (DGNs) have powerful inference and recovery capabilities, we use one hemisphere to predict the opposite hemisphere by training the DGNs, which automatically fit the built-in dependencies between the left and right hemispheres. After training, the reconstructed images approximate the homologous components in the hemisphere. We use the difference between the actual and reconstructed hemispheres to measure hemisphere-specific components due to asymmetric expression of environmental and genetic factors. The results show that our model is biologically plausible and that our proposed metric of hemispheric specialization is reliable, representing a wide range of individual variation. Together, this work provides promising tools for exploring brain asymmetry and new insights into self-supervised DGNs for representing the brain.

An executive-functions-based reading training enhances sensory-motor systems integration during reading fluency in children with dyslexia

The Simple View of Reading model suggests that intact language processing and word decoding lead to proficient reading comprehension, with recent studies pointing at executive functions as an important component contributing to reading proficiency. Here, we aimed to determine the underlying mechanism(s) for these changes. Participants include 120 8- to 12-year-old children (n = 55 with dyslexia, n = 65 typical readers) trained on an executive functions-based reading program, including pre/postfunctional MRI and behavioral data collection. Across groups, improved word reading was related to stronger functional connections within executive functions and sensory networks. In children with dyslexia, faster and more accurate word reading was related to stronger functional connections within and between sensory networks. These results suggest greater synchronization of brain systems after the intervention, consistent with the "neural noise" hypothesis in children with dyslexia and support the consideration of including executive functions as part of the Simple View of Reading model.

Brain network connectivity during peer evaluation in adolescent females: Associations with age, pubertal hormones, timing, and status

Despite copious data linking brain function with changes to social behavior and mental health, little is known about how puberty relates to brain functioning. We investigated the specificity of brain network connectivity associations with pubertal indices and age to inform neurodevelopmental models of adolescence. We examined how brain network connectivity during a peer evaluation fMRI task related to pubertal hormones (dehydroepiandrosterone and testosterone), pubertal timing and status, and age. Participants were 99 adolescents assigned female at birth aged 9-15 (M = 12.38, SD = 1.81) enriched for the presence of internalizing symptoms. Multivariate analysis revealed that within Salience, between Frontoparietal - Reward and Cinguloopercular - Reward network connectivity were associated with all measures of pubertal development and age. Specifically, Salience connectivity linked with age, pubertal hormones, and status, but not timing. In contrast, Frontoparietal - Reward connectivity was only associated with hormones. Finally, Cinguloopercular - Reward connectivity related to age and pubertal status, but not hormones or timing. These results provide evidence that the salience processing underlying peer evaluation is jointly influenced by various indices of puberty and age, while coordination between cognitive control and reward circuitry is related to pubertal hormones, pubertal status, and age in unique ways.

Community detection in the human connectome: Method types, differences and their impact on inference

Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI fromn $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), andn $$ n $$ = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.

How do tasks impact the reliability of fMRI functional connectivity?

While there is growing interest in the use of functional magnetic resonance imaging-functional connectivity (fMRI-FC) for biomarker research, low measurement reliability of conventional acquisitions may limit applications. Factors known to impact FC reliability include scan length, head motion, signal properties, such as temporal signal-to-noise ratio (tSNR), and the acquisition state or task. As tasks impact signal in a region-wise fashion, they likely impact FC reliability differently across the brain, making task an important decision in study design. Here, we use the densely sampled Midnight Scan Club (MSC) dataset, comprising 5 h of rest and 6 h of task fMRI data in 10 healthy adults, to investigate regional effects of tasks on FC reliability. We further considered how BOLD signal properties contributing to tSNR, that is, temporal mean signal (tMean) and temporal standard deviation (tSD), vary across the brain, associate with FC reliability, and are modulated by tasks. We found that, relative to rest, tasks enhanced FC reliability and increased tSD for specific task-engaged regions. However, FC signal variability and reliability is broadly dampened during tasks outside task-engaged regions. From our analyses, we observed signal variability was the strongest driver of FC reliability. Overall, our findings suggest that the choice of task can have an important impact on reliability and should be considered in relation to maximizing reliability in networks of interest as part of study design.

Distinct functional subnetworks of cognitive domains in older adults with minor cognitive deficits

Although past research has established a relationship between functional connectivity and cognitive function, less is known about which cognitive domains are associated with which specific functional networks. This study investigated associations between functional connectivity and global cognitive function and performance in the domains of memory, executive function and psychomotor speed in 166 older adults aged 75-91 years (mean = 80.3 ± 3.8) with minor cognitive deficits (Mini-Mental State Examination scores between 21 and 27). Functional connectivity was assessed within 10 standard large-scale resting-state networks and on a finer spatial resolution between 300 nodes in a functional connectivity matrix. No domain-specific associations with mean functional connectivity within large-scale resting-state networks were found. Node-level analysis revealed that associations between functional connectivity and cognitive performance differed across cognitive functions in strength, location and direction. Specific subnetworks of functional connections were found for each cognitive domain in which higher connectivity between some nodes but lower connectivity between other nodes were related to better cognitive performance. Our findings add to a growing body of literature showing differential sensitivity of functional connections to specific cognitive functions and may be a valuable resource for hypothesis generation of future studies aiming to investigate specific cognitive dysfunction with resting-state functional connectivity in people with beginning cognitive deficits.

Functional MRI of the Brainstem for Assessing Its Autonomic Functions: From Imaging Parameters and Analysis to Functional Atlas

BACKGROUND

The brainstem is a crucial component of the central autonomic nervous (CAN) system. Functional MRI (fMRI) of the brainstem remains challenging due to a range of factors, including diverse imaging protocols, analysis, and interpretation.

PURPOSE

To develop an fMRI protocol for establishing a functional atlas in the brainstem.

STUDY TYPE

Prospective cross-sectional study.

SUBJECTS

Ten healthy subjects (four males, six females).

FIELD STRENGTH/SEQUENCE

Using a 3.0 Tesla MR scanner, we acquired T1-weighted images and three different fMRI scans using fMRI protocols of the optimized functional Imaging of Brainstem (FIBS), the Human Connectome Project (HCP), and the Adolescent Brain Cognitive Development (ABCD) project.

ASSESSMENT

The temporal signal-to-noise-ratio (TSNR) of fMRI data was compared between the FIBS, HCP, and ABCD protocols. Additionally, the main normalization algorithms (i.e., FSL-FNIRT, SPM-DARTEL, and ANTS-SyN) were compared to identify the best approach to normalize brainstem data using root-mean-square (RMS) error computed based on manually defined reference points. Finally, a functional autonomic brainstem atlas that maps brainstem regions involved in the CAN system was defined using meta-analysis and data-driven approaches.

STATISTICAL TESTS

ANOVA was used to compare the performance of different imaging and preprocessing pipelines with multiple comparison corrections (P ≤ 0.05). Dice coefficient estimated ROI overlap, with 50% overlap between ROIs identified in each approach considered significant.

RESULTS

The optimized FIBS protocol showed significantly higher brainstem TSNR than the HCP and ABCD protocols (P ≤ 0.05). Furthermore, FSL-FNIRT RMS error (2.1 ± 1.22 mm; P ≤ 0.001) exceeded SPM (1.5 ± 0.75 mm; P ≤ 0.01) and ANTs (1.1 ± 0.54 mm). Finally, a set of 12 final brainstem ROIs with dice coefficient ≥0.50, as a step toward the development of a functional brainstem atlas.

DATA CONCLUSION

The FIBS protocol yielded more robust brainstem CAN results and outperformed both the HCP and ABCD protocols.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

Individualized functional connectivity markers associated with motor and mood symptoms of Parkinson's disease

Parkinson's disease (PD) is a complex neurological disorder characterized by many motor and non-motor symptoms. While most studies focus on the motor symptoms of the disease, it is important to identify markers that underlie different facets of the disease. In this case-control study, we sought to discover reliable, individualized functional connectivity markers associated with both motor and mood symptoms of PD. Using functional MRI, we extensively sampled 166 patients with PD (64 women, 102 men; mean age=61.8 years, SD=7.81) and 51 healthy control participants (32 women, 19 men; mean age=55.68 years, SD=7.62). We found that a model consisting of 44 functional connections predicted both motor (UPDRS-III: Pearson r=0.21, FDR-adjusted p=0.006) and mood symptoms (HAMD: Pearson r=0.23, FDR-adjusted p=0.006; HAMA: Pearson r=0.21, FDR-adjusted p=0.006). Two sets of connections contributed differentially to these predictions. Between-network connections, mainly connecting the sensorimotor and visual large-scale functional networks, substantially contributed to the prediction of motor measures, while within-network connections in the insula and sensorimotor network contributed more so to mood prediction. The middle to posterior insula region played a particularly important role in predicting depression and anxiety scores. We successfully replicated and generalized our findings in two independent PD datasets. Taken together, our findings indicate that sensorimotor and visual network markers are indicative of PD brain pathology, and that distinct subsets of markers are associated with motor and mood symptoms of PD.

Functional network disorganization and cognitive decline following fractionated whole-brain radiation in mice

Cognitive dysfunction following radiotherapy (RT) is one of the most common complications associated with RT delivered to the brain, but the precise mechanisms behind this dysfunction are not well understood, and to date, there are no preventative measures or effective treatments. To improve patient outcomes, a better understanding of the effects of radiation on the brain's functional systems is required. Functional magnetic resonance imaging (fMRI) has shown promise in this regard, however, compared to neural activity, hemodynamic measures of brain function are slow and indirect. Understanding how RT acutely and chronically affects functional brain organization requires more direct examination of temporally evolving neural dynamics as they relate to cerebral hemodynamics for bridging with human studies. In order to adequately study the underlying mechanisms of RT-induced cognitive dysfunction, the development of clinically mimetic RT protocols in animal models is needed. To address these challenges, we developed a fractionated whole-brain RT protocol (3Gy/day for 10 days) and applied longitudinal wide field optical imaging (WFOI) of neural and hemodynamic brain activity at 1, 2, and 3 months post RT. At each time point, mice were subject to repeated behavioral testing across a variety of sensorimotor and cognitive domains. Disruptions in cortical neuronal and hemodynamic activity observed 1 month post RT were significantly worsened by 3 months. While broad changes were observed in functional brain organization post RT, brain regions most impacted by RT occurred within those overlapping with the mouse default mode network and other association areas similar to prior reports in human subjects. Further, significant cognitive deficits were observed following tests of novel object investigation and responses to auditory and contextual cues after fear conditioning. Our results fill a much-needed gap in understanding the effects of whole-brain RT on systems level brain organization and how RT affects neuronal versus hemodynamic signaling in the cortex. Having established a clinically-relevant injury model, future studies can examine therapeutic interventions designed to reduce neuroinflammation-based injury following RT. Given the overlap of sequelae that occur following RT with and without chemotherapy, these tools can also be easily incorporated to examine chemotherapy-related cognitive impairment.

Neuroimmune mechanisms connecting violence with internalizing symptoms: A high-dimensional multimodal mediation analysis

Violence exposure is associated with worsening anxiety and depression symptoms among adolescents. Mechanistically, social defeat stress models in mice indicate that violence increases peripherally derived macrophages in threat appraisal regions of the brain, which have been causally linked to anxious behavior. In the present study, we investigate if there is a path connecting violence exposure with internalizing symptom severity through peripheral inflammation and amygdala connectivity. Two hundred and thirty-three adolescents, ages 12-15, from the Chicago area completed clinical assessments, immune assays and neuroimaging. A high-dimensional multimodal mediation model was fit, using violence exposure as the predictor, 12 immune variables as the first set of mediators and 288 amygdala connectivity variables as the second set, and internalizing symptoms as the primary outcome measure. 56.2% of the sample had been exposed to violence in their lifetime. Amygdala-hippocampus connectivity mediated the association between violence exposure and internalizing symptoms (ζ ̂ Hipp π ̂ Hipp = 0.059 $$ {\hat{\zeta}}_{\mathrm{Hipp}}{\hat{\pi}}_{\mathrm{Hipp}}=0.059 $$ ,95 % CI boot = 0.009,0.134 $$ 95\%{\mathrm{CI}}_{\mathrm{boot}}=\left[\mathrm{0.009,0.134}\right] $$ ). There was no evidence that inflammation or inflammation and amygdala connectivity in tandem mediated the association. Considering the amygdala and the hippocampus work together to encode, consolidate, and retrieve contextual fear memories, violence exposure may be associated with greater connectivity between the amygdala and the hippocampus because it could be adaptive for the amygdala and the hippocampus to be in greater communication following violence exposure to facilitate evaluation of contextual threat cues. Therefore, chronic elevations of amygdala-hippocampal connectivity may indicate persistent vigilance that leads to internalizing symptoms.

Changes in Brain Network Connections After Exposure and Response Prevention Therapy for Obsessive-Compulsive Disorder in Adolescents and Adults

BACKGROUND

Functional alterations of tripartite neural networks during cognitive control (i.e., frontoparietal network [FPN], cingulo-opercular network, and default mode network) occur in patients with obsessive-compulsive disorder (OCD) and may contribute to illness expression. However, the degree to which changes in these networks are elicited by gold standard treatment (e.g., exposure and response prevention [EX/RP]) remains unknown. Understanding how EX/RP modulates network connectivity in adolescent versus adult patients with OCD may aid the identification of developmentally sensitive treatment targets that enhance cognitive control.

METHODS

Data from a total of 169 adolescents (13-17 years) and adults (25-40 years; 57% female) were analyzed, including healthy control participants (n = 58) and patients with OCD (n = 111) who were randomized to either EX/RP or an active control therapy (stress management training). Participants performed a flanker task during functional magnetic resonance imaging pre- and posttreatment. To retain sensitivity to individual differences in connectivity, group iterative multiple model estimation was used to assess functional connectivity (i.e., density) within and between brain networks.

RESULTS

Significant increases in FPN density and decreases in FPN-default mode network density were observed from pre- to posttreatment in patients who received EX/RP. The opposite patterns of change occurred in patients who received stress management training. These treatment-related changes in network density did not differ across age group.

CONCLUSIONS

Results suggest EX/RP-specific changes in task-based connectivity in patients with OCD. Given baseline differences between healthy control participants and patients by age group, these treatment-related changes may indicate restoration of healthy FPN and default mode network development across patients, providing targets for improving response to EX/RP.

Cognitive deficits and altered functional brain network organization in pediatric brain tumor patients

Survivors of pediatric brain tumors experience significant cognitive deficits from their diagnosis and treatment. The exact mechanisms of cognitive injury are poorly understood, and validated predictors of long-term cognitive outcome are lacking. Resting state functional magnetic resonance imaging allows for the study of the spontaneous fluctuations in bulk neural activity, providing insight into brain organization and function. Here, we evaluated cognitive performance and functional network architecture in pediatric brain tumor patients. Forty-nine patients (7-18 years old) with a primary brain tumor diagnosis underwent resting state imaging during regularly scheduled clinical visits. All patients were tested with a battery of cognitive assessments. Extant data from 139 typically developing children were used as controls. We found that obtaining high-quality imaging data during routine clinical scanning was feasible. Functional network organization was significantly altered in patients, with the largest disruptions observed in patients who received propofol sedation. Awake patients demonstrated significant decreases in association network segregation compared to controls. Interestingly, there was no difference in the segregation of sensorimotor networks. With a median follow-up of 3.1 years, patients demonstrated cognitive deficits in multiple domains of executive function. Finally, there was a weak correlation between decreased default mode network segregation and poor picture vocabulary score. Future work with longer follow-up, longitudinal analyses, and a larger cohort will provide further insight into this potential predictor.

Covariance patterns between sleep health domains and distributed intrinsic functional connectivity

Sleep health is both conceptually and operationally a composite concept containing multiple domains of sleep. In line with this, high dependence and interaction across different domains of sleep health encourage a transition in sleep health research from categorical to dimensional approaches that integrate neuroscience and sleep health. Here, we seek to identify the covariance patterns between multiple sleep health domains and distributed intrinsic functional connectivity by applying a multivariate approach (partial least squares). This multivariate analysis reveals a composite sleep health dimension co-varying with connectivity patterns involving the attentional and thalamic networks and which appear relevant at the neuromolecular level. These findings are further replicated and generalized to several unseen independent datasets. Critically, the identified sleep-health related connectome shows diagnostic potential for insomnia disorder. These results together delineate a potential brain connectome biomarker for sleep health with high potential for clinical translation.

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer disease

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer Disease (AD). Given their essential role in neural communication, disruptions to these hubs have profound implications for overall brain network integrity and functionality. Hub disruption, or targeted impairment of functional connectivity at the hubs, is recognized in AD patients. Computational models paired with evidence from animal experiments hint at a mechanistic explanation, suggesting that these hubs may be preferentially targeted in neurodegeneration, due to their high neuronal activity levels-a phenomenon termed "activity-dependent degeneration". Yet, two critical issues were unresolved. First, past research hasn't definitively shown whether hub regions face a higher likelihood of impairment (targeted attack) compared to other regions or if impairment likelihood is uniformly distributed (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We applied a refined hub disruption index to determine the presence of targeted attacks in AD. Furthermore, we explored potential evidence for activity-dependent degeneration by evaluating if hub vulnerability is better explained by global connectivity or connectivity variations across functional systems, as well as comparing its timing relative to amyloid beta deposition in the brain. Our unique cohort of participants with autosomal dominant Alzheimer Disease (ADAD) allowed us to probe into the preclinical stages of AD to determine the hub disruption timeline in relation to expected symptom emergence. Our findings reveal a hub disruption pattern in ADAD aligned with targeted attacks, detectable even in pre-clinical stages. Notably, the disruption's severity amplified alongside symptomatic progression. Moreover, since excessive local neuronal activity has been shown to increase amyloid deposition and high connectivity regions show high level of neuronal activity, our observation that hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in Aβ PET cortical markers is consistent with the activity-dependent degeneration model. Intriguingly, these disruptions were discernible 8 years before the expected age of symptom onset. Taken together, our findings not only align with the targeted attack on hubs model but also suggest that activity-dependent degeneration might be the cause of hub vulnerability. This deepened understanding could be instrumental in refining diagnostic techniques and developing targeted therapeutic strategies for AD in the future.

A Functional Connectome-Based Neural Signature for Individualized Prediction of Antipsychotic Response in First-Episode Psychosis

OBJECTIVE

Identification of robust biomarkers that predict individualized response to antipsychotic treatment at the early stage of psychotic disorders remains a challenge in precision psychiatry. The aim of this study was to investigate whether any functional connectome-based neural traits could serve as such a biomarker.

METHODS

In a discovery sample, 49 patients with first-episode psychosis received multi-paradigm fMRI scans at baseline and were clinically followed up for 12 weeks under antipsychotic monotherapies. Treatment response was evaluated at the individual level based on the psychosis score of the Brief Psychiatric Rating Scale. Cross-paradigm connectivity and connectome-based predictive modeling were employed to train a predictive model that uses baseline connectomic measures to predict individualized change rates of psychosis scores, with model performance evaluated as the Pearson correlations between the predicted change rates and the observed change rates, based on cross-validation. The model generalizability was further examined in an independent validation sample of 24 patients in a similar design.

RESULTS

The results revealed a paradigm-independent connectomic trait that significantly predicted individualized treatment outcome in both the discovery sample (predicted-versus-observed r=0.41) and the validation sample (predicted-versus-observed r=0.47, mean squared error=0.019). Features that positively predicted psychosis change rates primarily involved connections related to the cerebellar-cortical circuitry, and features that negatively predicted psychosis change rates were chiefly connections within the cortical cognitive systems.

CONCLUSIONS

This study discovers and validates a connectome-based functional signature as a promising early predictor for individualized response to antipsychotic treatment in first-episode psychosis, thus highlighting the potential clinical value of this biomarker in precision psychiatry.

Within-Individual BOLD Signal Variability and its Implications for Task-Based Cognition: A Systematic Review

Within-individual blood oxygen level-dependent (BOLD) signal variability, intrinsic moment-to-moment signal fluctuations within a single individual in specific voxels across a given time course, is a relatively new metric recognized in the neuroimaging literature. Within-individual BOLD signal variability has been postulated to provide information beyond that provided by mean-based analysis. Synthesis of the literature using within-individual BOLD signal variability methodology to examine various cognitive domains is needed to understand how intrinsic signal fluctuations contribute to optimal performance. This systematic review summarizes and integrates this literature to assess task-based cognitive performance in healthy groups and few clinical groups. Included papers were published through October 17, 2022. Searches were conducted on PubMed and APA PsycInfo. Studies eligible for inclusion used within-individual BOLD signal variability methodology to examine BOLD signal fluctuations during task-based functional magnetic resonance imaging (fMRI) and/or examined relationships between task-based BOLD signal variability and out-of-scanner behavioral measure performance, were in English, and were empirical research studies. Data from each of the included 19 studies were extracted and study quality was systematically assessed. Results suggest that variability patterns for different cognitive domains across the lifespan (ages 7-85) may depend on task demands, measures, variability quantification method used, and age. As neuroimaging methods explore individual-level contributions to cognition, within-individual BOLD signal variability may be a meaningful metric that can inform understanding of neurocognitive performance. Further research in understudied domains/populations, and with consistent quantification methods/cognitive measures, will help conceptualize how intrinsic BOLD variability impacts cognitive abilities in healthy and clinical groups.

Long-term Intensive Soccer Training Induced Dynamic Reconfiguration of Brain Network

Long-term motor skill learning has been shown to impact the functional plasticity of the brain. Athletes, as a unique population, exhibit remarkable adaptive changes in the static properties of their brain networks. However, studying the differences between expert and novice athletes using a dynamic brain network framework can provide a fresh perspective on how motor skill learning affects the functional organization of the brain. In this study, we investigated the dynamic properties of brain networks in expert and novice soccer players at the whole-brain, network, and region-based levels. Our findings revealed that expert soccer players displayed reduced integration and increased segregation at the whole-brain level. As for network level, experts exhibited increased segregation and reduced flexibility in the visual network, enhanced integration between the visual and ventral attention networks, and decreased integration in the subcortical-sensorimotor and subcortical-cerebellar networks. Additionally, specific brain regions within the visual network exhibited greater recruitment in expert soccer players compared to novices at the nodal level. Furthermore, classification analyses demonstrated the critical role played by the visual network in the classification process. In conclusion, our study provides new insights into the dynamic properties of brain networks in expert and novice soccer players, and suggests that reduced integration and increased segregation in the visual network may be neuroimaging marker that distinguish expert soccer players from novices. Our findings may have implications for the training and development of athletes and advance our understanding of how motor skill learning affects brain functional organization.

Echoes from Intrinsic Connectivity Networks in the Subcortex

Decades of research have greatly improved our understanding of intrinsic human brain organization in terms of functional networks and the transmodal hubs within the cortex at which they converge. However, substrates of multinetwork integration in the human subcortex are relatively uncharted. Here, we leveraged recent advances in subcortical atlasing and ultra-high field (7 T) imaging optimized for the subcortex to investigate the functional architecture of 14 individual structures in healthy adult males and females with a fully data-driven approach. We revealed that spontaneous neural activity in subcortical regions can be decomposed into multiple independent subsignals that correlate with, or "echo," the activity in functional networks across the cortex. Distinct subregions of the thalamus, striatum, claustrum, and hippocampus showed a varied pattern of echoes from attention, control, visual, somatomotor, and default mode networks, demonstrating evidence for a heterogeneous organization supportive of functional integration. Multiple network activity furthermore converged within the globus pallidus externa, substantia nigra, and ventral tegmental area but was specific to one subregion, while the amygdala and pedunculopontine nucleus preferentially affiliated with a single network, showing a more homogeneous topography. Subregional connectivity of the globus pallidus interna, subthalamic nucleus, red nucleus, periaqueductal gray, and locus coeruleus did not resemble patterns of cortical network activity. Together, these finding describe potential mechanisms through which the subcortex participates in integrated and segregated information processing and shapes the spontaneous cognitive dynamics during rest.SIGNIFICANCE STATEMENT Despite the impact of subcortical dysfunction on brain health and cognition, large-scale functional mapping of subcortical structures severely lags behind that of the cortex. Recent developments in subcortical atlasing and imaging at ultra-high field provide new avenues for studying the intricate functional architecture of the human subcortex. With a fully data-driven analysis, we reveal subregional connectivity profiles of a large set of noncortical structures, including those rarely studied in fMRI research. The results have implications for understanding how the functional organization of the subcortex facilitates integrative processing through cross-network information convergence, paving the way for future work aimed at improving our knowledge of subcortical contributions to intrinsic brain dynamics and spontaneous cognition.

Explaining Deep Learning-Based Representations of Resting State Functional Connectivity Data: Focusing on Interpreting Nonlinear Patterns in Autism Spectrum Disorder

Background

Resting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used to study brain function in psychiatric disorders, yielding insight into brain organization. However, the high dimensionality of the rs-fMRI data presents challenges, and requires dimensionality reduction before applying machine learning techniques. Neural networks, specifically variational autoencoders (VAEs), have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity patterns, addressing the complex nonlinear structure of rs-fMRI. However, interpreting those latent representations remains a challenge. This paper aims to address this gap by creating explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD).

Methods

One-thousand one hundred and fifty participants (601 HC and 549 patients with ASD) were included in the analysis. We extracted functional connectivity correlation matrices from the preprocessed rs-fMRI data using Power atlas with 264 ROIs. Then VAEs were trained in an unsupervised fashion. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures.

Results

We quantified the latent contribution scores for the ASD and control groups at the network level. We found that both ASD and control groups share the top network connectivity that contribute to all estimated latent components. For example, latent 0 was driven by resting state functional connectivity patterns (rsFC) within ventral attention network in both the ASD and control. However, significant differences in the latent contribution scores between the ASD and control groups were discovered within the ventral attention network in latent 0 and the sensory/somatomotor network in latent 2.

Conclusion

This study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC features as estimated latent representation changes, enabling an explainable deep learning model to better understand the underlying neural mechanism of ASD.

Effects of phase encoding direction on test-retest reliability of human functional connectome

The majority of human connectome studies in the literature based on functional magnetic resonance imaging (fMRI) data use either an anterior-to-posterior (AP) or a posterior-to-anterior (PA) phase encoding direction (PED). However, whether and how PED would affect test-retest reliability of functional connectome is unclear. Here, in a sample of healthy subjects with two sessions of fMRI scans separated by 12 weeks (two runs per session, one with AP, the other with PA), we tested the influence of PED on global, nodal, and edge connectivity in the constructed brain networks. All data underwent the state-of-the-art Human Connectome Project (HCP) pipeline to correct for phase-encoding-related distortions before entering analysis. We found that at the global level, the PA scans showed significantly higher intraclass correlation coefficients (ICCs) for global connectivity compared with AP scans, which was particularly prominent when using the Seitzman-300 atlas (versus the CAB-NP-718 atlas). At the nodal level, regions most strongly affected by PED were consistently mapped to the cingulate cortex, temporal lobe, sensorimotor areas, and visual areas, with significantly higher ICCs during PA scans compared with AP scans, regardless of atlas. Better ICCs were also observed during PA scans at the edge level, in particular when global signal regression (GSR) was not performed. Further, we demonstrated that the observed reliability differences between PEDs may relate to a similar effect on the reliability of temporal signal-to-noise ratio (tSNR) in the same regions (that PA scans were associated with higher reliability of tSNR than AP scans). Averaging the connectivity outcome from the AP and PA scans could increase median ICCs, especially at the nodal and edge levels. Similar results at the global and nodal levels were replicated in an independent, public dataset from the HCP-Early Psychosis (HCP-EP) study with a similar design but a much shorter scan session interval. Our findings suggest that PED has significant effects on the reliability of connectomic estimates in fMRI studies. We urge that these effects need to be carefully considered in future neuroimaging designs, especially in longitudinal studies such as those related to neurodevelopment or clinical intervention.

Mild traumatic brain injury history is associated with lower brain network resilience in soldiers

Special Operations Forces combat soldiers sustain frequent blast and blunt neurotrauma, most often classified as mild traumatic brain injuries. Exposure to repetitive mild traumatic brain injuries is associated with persistent behavioural, cognitive, emotional and neurological symptoms later in life. Identifying neurophysiological changes associated with mild traumatic brain injury exposure, in the absence of present-day symptoms, is necessary for detecting future neurological risk. Advancements in graph theory and functional MRI have offered novel ways to analyse complex whole-brain network connectivity. Our purpose was to determine how mild traumatic brain injury history, lifetime incidence and recency affected whole-brain graph theoretical outcome measures. Healthy male Special Operations Forces combat soldiers (age = 33.2 ± 4.3 years) underwent multimodal neuroimaging at a biomedical research imaging centre using 3T Siemens Prisma or Biograph MRI scanners in this cross-sectional study. Anatomical and functional scans were preprocessed. The blood-oxygen-level-dependent signal was extracted from each functional MRI time series using the Big Brain 300 atlas. Correlations between atlas regions were calculated and Fisher z-transformed to generate subject-level correlation matrices. The Brain Connectivity Toolbox was used to obtain functional network measures for global efficiency (the average inverse shortest path length), local efficiency (the average global efficiency of each node and its neighbours), and assortativity coefficient (the correlation coefficient between the degrees of all nodes on two opposite ends of a link). General linear models were fit to compare mild traumatic brain injury lifetime incidence and recency. Nonparametric ANOVAs were used for tests on non-normally distributed data. Soldiers with a history of mild traumatic brain injury had significantly lower assortativity than those who did not self-report mild traumatic brain injury (t148 = 2.44, P = 0.016). The assortativity coefficient was significantly predicted by continuous mild traumatic brain injury lifetime incidence [F1,144 = 6.51, P = 0.012]. No differences were observed between recency groups, and no global or local efficiency differences were observed between mild traumatic brain injury history and lifetime incidence groups. Brain networks with greater assortativity have more resilient, interconnected hubs, while those with lower assortativity indicate widely distributed, vulnerable hubs. Greater lifetime mild traumatic brain injury incidence predicted lower assortativity in our study sample. Less resilient brain networks may represent a lack of physiological recovery in mild traumatic brain injury patients, who otherwise demonstrate clinical recovery, more vulnerability to future brain injury and increased risk for accelerated age-related neurodegenerative changes. Future longitudinal studies should investigate whether decreased brain network resilience may be a predictor for long-term neurological dysfunction.

Glioblastoma induces whole-brain spectral change in resting state fMRI: Associations with clinical comorbidities and overall survival

Glioblastoma, a highly aggressive form of brain tumor, is a brain-wide disease. We evaluated the impact of tumor burden on whole brain resting-state functional magnetic resonance imaging (rs-fMRI) activity. Specifically, we analyzed rs-fMRI signals in the temporal frequency domain in terms of the power-law exponent and fractional amplitude of low-frequency fluctuations (fALFF). We contrasted 189 patients with newly-diagnosed glioblastoma versus 189 age-matched healthy reference participants from an external dataset. The patient and reference datasets were matched for age and head motion. The principal finding was markedly flatter spectra and reduced grey matter fALFF in the patients as compared to the reference dataset. We posit that the whole-brain spectral change is attributable to global dysregulation of excitatory and inhibitory balance and metabolic demand in the tumor-bearing brain. Additionally, we observed that clinical comorbidities, in particular, seizures, and MGMT promoter methylation, were associated with flatter spectra. Notably, the degree of change in spectra was predictive of overall survival. Our findings suggest that frequency domain analysis of rs-fMRI activity provides prognostic information in glioblastoma patients and offers a means of noninvasively studying the effects of glioblastoma on the whole brain.

Brain network decoupling with increased serum neurofilament and reduced cognitive function in Alzheimer's disease

Neurofilament light chain, a putative measure of neuronal damage, is measurable in blood and CSF and is predictive of cognitive function in individuals with Alzheimer's disease. There has been limited prior work linking neurofilament light and functional connectivity, and no prior work has investigated neurofilament light associations with functional connectivity in autosomal dominant Alzheimer's disease. Here, we assessed relationships between blood neurofilament light, cognition, and functional connectivity in a cross-sectional sample of 106 autosomal dominant Alzheimer's disease mutation carriers and 76 non-carriers. We employed an innovative network-level enrichment analysis approach to assess connectome-wide associations with neurofilament light. Neurofilament light was positively correlated with deterioration of functional connectivity within the default mode network and negatively correlated with connectivity between default mode network and executive control networks, including the cingulo-opercular, salience, and dorsal attention networks. Further, reduced connectivity within the default mode network and between the default mode network and executive control networks was associated with reduced cognitive function. Hierarchical regression analysis revealed that neurofilament levels and functional connectivity within the default mode network and between the default mode network and the dorsal attention network explained significant variance in cognitive composite scores when controlling for age, sex, and education. A mediation analysis demonstrated that functional connectivity within the default mode network and between the default mode network and dorsal attention network partially mediated the relationship between blood neurofilament light levels and cognitive function. Our novel results indicate that blood estimates of neurofilament levels correspond to direct measurements of brain dysfunction, shedding new light on the underlying biological processes of Alzheimer's disease. Further, we demonstrate how variation within key brain systems can partially mediate the negative effects of heightened total serum neurofilament levels, suggesting potential regions for targeted interventions. Finally, our results lend further evidence that low-cost and minimally invasive blood measurements of neurofilament may be a useful marker of brain functional connectivity and cognitive decline in Alzheimer's disease.

The CBIT + TMS trial: study protocol for a two-phase randomized controlled trial testing neuromodulation to augment behavior therapy for youth with chronic tics

BACKGROUND

Comprehensive Behavioral Intervention for Tics (CBIT) is a first-line treatment for tic disorders that aims to improve controllability over tics that an individual finds distressing or impairing. However, it is only effective for approximately half of patients. Supplementary motor area (SMA)-directed neurocircuitry plays a strong role in motor inhibition, and activity in this region is thought to contribute to tic expression. Targeted modulation of SMA using transcranial magnetic stimulation (TMS) may increase CBIT efficacy by improving patients' ability to implement tic controllability behaviors.

METHODS

The CBIT + TMS trial is a two-phase, milestone-driven early-stage randomized controlled trial. The trial will test whether augmenting CBIT with inhibitory, non-invasive stimulation of SMA with TMS modifies activity in SMA-mediated circuits and enhances tic controllability in youth ages 12-21 years with chronic tics. Phase 1 will directly compare two rTMS augmentation strategies (1 Hz rTMS vs. cTBS) vs. sham in N = 60 participants. Quantifiable, a priori "Go/No Go Criteria" guide the decision to proceed to phase 2 and the selection of the optimal TMS regimen. Phase 2 will compare the optimal regimen vs. sham and test the link between neural target engagement and clinical outcomes in a new sample of N = 60 participants.

DISCUSSION

This clinical trial is one of few to date testing TMS augmentation of therapy in a pediatric sample. The results will provide insight into whether TMS is a potentially viable strategy for enhancing CBIT efficacy and reveal potential neural and behavioral mechanisms of change.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04578912 . Registered on October 8, 2020.

Large-scale encoding of emotion concepts becomes increasingly similar between individuals from childhood to adolescence

Humans require a shared conceptualization of others' emotions for adaptive social functioning. A concept is a mental blueprint that gives our brains parameters for predicting what will happen next. Emotion concepts undergo refinement with development, but it is not known whether their neural representations change in parallel. Here, in a sample of 5-15-year-old children (n = 823), we show that the brain represents different emotion concepts distinctly throughout the cortex, cerebellum and caudate. Patterns of activation to each emotion changed little across development. Using a model-free approach, we show that activation patterns were more similar between older children than between younger children. Moreover, scenes that required inferring negative emotional states elicited higher default mode network activation similarity in older children than younger children. These results suggest that representations of emotion concepts are relatively stable by mid to late childhood and synchronize between individuals during adolescence.

The body mass index is associated with increased temporal variability of functional connectivity in brain reward system

The reward system has been proven to be contributed to the vulnerability of obesity. Previous fMRI studies have shown abnormal functional connectivity of the reward system in obesity. However, most studies were based on static index such as resting-state functional connectivity (FC), ignoring the dynamic changes over time. To investigate the dynamic neural correlates of obesity susceptibility, we used a large, demographically well-characterized sample from the Human Connectome Project (HCP) to determine the relationship of body mass index (BMI) with the temporal variability of FC from integrated multilevel perspectives, i.e., regional and within- and between-network levels. Linear regression analysis was used to investigate the association between BMI and temporal variability of FC, adjusting for covariates of no interest. We found that BMI was positively associated with regional FC variability in reward regions, such as the ventral orbitofrontal cortex and visual regions. At the intra-network level, BMI was positively related to the variability of FC within the limbic network (LN) and default mode network (DMN). At the inter-network level, variability of connectivity of LN with DMN, frontoparietal, sensorimotor, and ventral attention networks showed positive correlations with BMI. These findings provided novel evidence for abnormal dynamic functional interaction between the reward network and the rest of the brain in obesity, suggesting a more unstable state and over-frequent interaction of the reward network and other attention and cognitive networks. These findings, thus, provide novel insight into obesity interventions that need to decrease the dynamic interaction between reward networks and other brain networks through behavioral treatment and neural modulation.

Combined Cognitive Training and Vortioxetine Mitigates Age-Related Declines in Functional Brain Network Integrity

OBJECTIVE

Age-related cognitive decline is common and potentially modifiable with cognitive training. Combining cognitive training with pro-cognitive medication offers an opportunity to modify brain networks to mitigate age-related cognitive decline. We tested the hypothesis that the efficacy of cognitive training could be amplified by combining it with vortioxetine, a pro-cognitive and pro-neuroplastic multimodal antidepressant.

METHODS

We evaluated the effects of 6 months of computerized cognitive training plus vortioxetine (versus placebo) on resting state functional connectivity in older adults (age 65+) with age-related cognitive decline. We first evaluated the association of functional connectivity with age and cognitive performance (N = 66). Then we compared the effects of vortioxetine plus cognitive training versus placebo plus cognitive training on connectivity changes over the training period (n = 20).

RESULTS

At baseline, greater age was significantly associated with lower within-network strength and network segregation, and poorer cognitive function. Cognitive training plus vortioxetine over 6 months positively impacted the relationship between age to mean network segregation. These effects were not observed in the placebo group. In contrast, vortioxetine did not modify the relationship of age to change in mean within-network strength. Exploratory analyses identified the cingulo-opercular network as the network most affected by cognitive training plus vortioxetine.

CONCLUSION

This preliminary study provides evidence that combining cognitive training with pro-cognitive medication may modulate the effects of aging on functional brain networks. Results indicate that for older adults experiencing age-related cognitive decline, vortioxetine has a potentially beneficial effect on the correspondence between aging and functional brain network segregation. These results await replication in a larger sample.

The elusive metric of lesion load

One of the widely used metrics in lesion-symptom mapping is lesion load that codes the amount of damage to a given brain region of interest. Lesion load aims to reduce the complex 3D lesion information into a feature that can reflect both site of damage, defined by the location of the region of interest, and size of damage within that region of interest. Basically, the process of estimation of lesion load converts a voxel-based lesion map into a region-based lesion map, with regions defined as atlas-based or data-driven spatial patterns. Here, after examining current definitions of lesion load, four methodological issues are discussed: (1) lesion load is agnostic to the location of damage within the region of interest, and it disregards damage outside the region of interest, (2) lesion load estimates are prone to errors introduced by the uncertainty in lesion delineation, spatial warping of the lesion/region, and binarization of the lesion/region, (3) lesion load calculation depends on brain parcellation selection, and (4) lesion load does not necessarily reflect a white matter disconnection. Overall, lesion load, when calculated in a robust way, can serve as a clinically-useful feature for explaining and predicting post-stroke outcome and recovery.

The Role of Extra-motor Networks in Upper Limb Motor Performance Post-stroke

BACKGROUND

Motor improvement post-stroke may happen even if resting state functional connectivity between the ipsilesional and contralesional components of the sensorimotor network is not fully recovered. Therefore, we investigated which extra-motor networks might support upper limb motor gains in response to treatment post-stroke.

METHODS

Both resting state functional connectivity and upper limb capacity were measured prior to and after an 8-week intervention of task-specific training in 29 human participants [59.24 ± (SD) 10.40 yrs., 12 females and 17 males] with chronic stroke. The sensorimotor and five extra-motor networks were defined: default mode, frontoparietal, cingulo-opercular, dorsal attention network, and salience networks. The Network Level Analysis toolbox was used to identify network pairs whose connectivities were enriched in connectome-behavior relationships.

RESULTS

Mean upper limb capacity score increased 5.45 ± (SD) 5.55 following treatment. Baseline connectivity of some motor but mostly extra-motor network interactions of cingulo-opercular and default-mode networks were predictive of upper limb capacity following treatment. Also, changes in connectivity for extra-motor interactions of salience with default mode, cingulo-opercular, and dorsal attention networks were correlated with gains in upper limb capacity.

CONCLUSIONS

These connectome-behavior patterns suggest larger involvement of cingulo-opercular networks in prediction of treatment response and of salience networks in maintenance of improved skilled behavior. These results support our hypothesis that cognitive networks may contribute to recovery of motor performance after stroke and provide additional insights into the neural correlates of intensive training.

Sex- and Age-Specific Deviations in Cerebellar Structure and Their Link With Symptom Dimensions and Clinical Outcome in Individuals at Clinical High Risk for Psychosis

BACKGROUND

The clinical high-risk (CHR) period offers a temporal window into neurobiological deviations preceding psychosis onset, but little attention has been given to regions outside the cerebrum in large-scale studies of CHR. Recently, the North American Prodrome Longitudinal Study (NAPLS)-2 revealed altered functional connectivity of the cerebello-thalamo-cortical circuitry among individuals at CHR; however, cerebellar morphology remains underinvestigated in this at-risk population, despite growing evidence of its involvement in psychosis.

STUDY DESIGN

In this multisite study, we analyzed T1-weighted magnetic resonance imaging scans obtained from N = 469 CHR individuals (61% male, ages = 12-36 years) and N = 212 healthy controls (52% male, ages = 12-34 years) from NAPLS-2, with a focus on cerebellar cortex and white matter volumes separately. Symptoms were rated by the Structured Interview for Psychosis-Risk Syndromes (SIPS). The outcome by two-year follow-up was categorized as in-remission, symptomatic, prodromal-progression, or psychotic. General linear models were used for case-control comparisons and tests for volumetric associations with baseline SIPS ratings and clinical outcomes.

STUDY RESULTS

Cerebellar cortex and white matter volumes differed between the CHR and healthy control groups at baseline, with sex moderating the difference in cortical volumes, and both sex and age moderating the difference in white matter volumes. Baseline ratings for major psychosis-risk dimensions as well as a clinical outcome at follow-up had tissue-specific associations with cerebellar volumes.

CONCLUSIONS

These findings point to clinically relevant deviations in cerebellar cortex and white matter structures among CHR individuals and highlight the importance of considering the complex interplay between sex and age when studying the neuromaturational substrates of psychosis risk.

Involvement of Thalamocortical Networks in Patients With Poststroke Thalamic Aphasia

BACKGROUND AND OBJECTIVE

Theories assume that thalamic stroke may cause aphasia because of dysfunction in connected cortical networks. This takes into account that brain functions are organized in distributed networks, and in turn, localized damage may result in a network disorder such as thalamic aphasia. With this study, we investigate whether the integration of the thalamus into specific thalamocortical networks underlies symptoms after thalamic stroke. We hypothesize that thalamic lesions in patients with language impairments are functionally connected to cortical networks for language and cognition.

METHODS

We combined nonparametric lesion mapping methods in a retrospective cohort of patients with acute or subacute first-ever thalamic stroke. A relationship between lesion location and language impairments was assessed using nonparametric voxel-based lesion-symptom mapping. This method reveals regions more frequently damaged in patients with compared with those without a symptom of interest. To test whether these symptoms are linked to a common thalamocortical network, we additionally performed lesion-network-symptom mapping. This method uses normative connectome data from resting-state fMRI of healthy participants (n = 65) for functional connectivity analyses, with lesion sites serving as seeds. Resulting lesion-dependent network connectivity of patients with language impairments was compared with those with motor and sensory deficits as baseline.

RESULTS

A total of 101 patients (mean [SD] age 64.1 [14.6] years, 57 left, 42 right, and 2 bilateral lesions) were included in the study. Voxel-based lesion-symptom mapping showed an association of language impairments with damage to left mediodorsal thalamic nucleus lesions. Lesion-network-symptom mapping revealed that language compared with sensory deficits were associated with higher normative lesion-dependent network connectivity to left frontotemporal language networks and bilateral prefrontal, insulo-opercular, midline cingular, and parietal domain-general networks. Lesions related to motor and sensory deficits showed higher lesion-dependent network connectivity within the sensorimotor network spanning prefrontal, precentral, and postcentral cortices.

DISCUSSION

Thalamic aphasia relates to lesions in the left mediodorsal thalamic nucleus and to functionally connected left cortical language and bilateral cortical networks for cognitive control. This suggests that dysfunction in thalamocortical networks contributes to thalamic aphasia. We propose that inefficient integration between otherwise undamaged domain-general and language networks may cause thalamic aphasia.

Resting state network mapping in individuals using deep learning

Introduction

Resting state functional MRI (RS-fMRI) is currently used in numerous clinical and research settings. The localization of resting state networks (RSNs) has been utilized in applications ranging from group analysis of neurodegenerative diseases to individual network mapping for pre-surgical planning of tumor resections. Reproducibility of these results has been shown to require a substantial amount of high-quality data, which is not often available in clinical or research settings.

Methods

In this work, we report voxelwise mapping of a standard set of RSNs using a novel deep 3D convolutional neural network (3DCNN). The 3DCNN was trained on publicly available functional MRI data acquired in n = 2010 healthy participants. After training, maps that represent the probability of a voxel belonging to a particular RSN were generated for each participant, and then used to calculate mean and standard deviation (STD) probability maps, which are made publicly available. Further, we compared our results to previously published resting state and task-based functional mappings.

Results

Our results indicate this method can be applied in individual subjects and is highly resistant to both noisy data and fewer RS-fMRI time points than are typically acquired. Further, our results show core regions within each network that exhibit high average probability and low STD.

Discussion

The 3DCNN algorithm can generate individual RSN localization maps, which are necessary for clinical applications. The similarity between 3DCNN mapping results and task-based fMRI responses supports the association of specific functional tasks with RSNs.

Preoperative functional connectivity by magnetic resonance imaging for refractory neocortical epilepsy

Objective

Patients with refractory epilepsy experience extensive and invasive clinical testing for seizure onset zones treatable by surgical resection. However, surgical resection can fail to provide therapeutic benefit, and patients with neocortical epilepsy have the poorest therapeutic outcomes. This case series studied patients with neocortical epilepsy who were referred for surgical treatment. Prior to surgery, patients volunteered for resting-state functional magnetic resonance imaging (rs-fMRI) in addition to imaging for the clinical standard of care. This work examined the variability of functional connectivity in patients, estimated from rs-fMRI, for associations with surgical outcomes.

Methods

This work examined pre-operative structural imaging, pre-operative rs-fMRI, and post-operative structural imaging from seven epilepsy patients. Review of the clinical record provided Engel classifications for surgical outcomes. A novel method assessed pre-operative rs-fMRI from patients using comparative rs-fMRI from a large cohort of healthy control subjects and estimated Gibbs distributions for functional connectivity in patients compared to healthy controls.

Results

Three patients had Engel classification Ia, one patient had Engel classification IIa, and three patients had Engel classification IV. Metrics for variability of functional connectivity, including absolute differences of the functional connectivity of each patient from healthy control averages and probabilistic scores for absolute differences, were higher for patients classified as Engel IV, for whom epilepsy surgery provided no meaningful improvement.

Significance

This work continues on-going efforts to use rs-fMRI to characterize abnormal functional connectivity in the brain. Preliminary evidence indicates that the topography of variant functional connectivity in epilepsy patients may be clinically relevant for identifying patients unlikely to have favorable outcomes after epilepsy surgery. Widespread topographic variations of functional connectivity also support the hypothesis that epilepsy is a disease of brain resting-state networks.

Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study

Background

Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored.

Methods

We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite.

Results

All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance.

Conclusions

Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences.

Funding

This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN).

Functional connectivity in older adults-the effect of cerebral small vessel disease

Ageing is associated with functional reorganization that is mainly characterized by declining functional connectivity due to general neurodegeneration and increasing incidence of disease. Functional connectivity has been studied across the lifespan; however, there is a paucity of research within the older groups (≥75 years) where neurodegeneration and disease prevalence are at its highest. In this cross-sectional study, we investigated associations between age and functional connectivity and the influence of cerebral small vessel disease (CSVD)-a common age-related morbidity-in 167 community-dwelling older adults aged 75-91 years (mean = 80.3 ± 3.8). Resting-state functional MRI was used to determine functional connectivity within ten standard networks and calculate the whole-brain graph theoretical measures global efficiency and clustering coefficient. CSVD features included white matter hyperintensities, lacunar infarcts, cerebral microbleeds, and atrophy that were assessed in each individual and a composite score was calculated. Both main and interaction effects (age*CSVD features) on functional connectivity were studied. We found stable levels of functional connectivity across the age range. CSVD was not associated with functional connectivity measures. To conclude, our data show that the functional architecture of the brain is relatively unchanged after 75 years of age and not differentially affected by individual levels of vascular pathology.

A pilot randomized sham controlled trial of bilateral iTBS for depression and executive function in older adults

INTRODUCTION

Executive function deficits (EFD) in late life depression (LLD) are associated with poor outcomes. Dysfunction of the cognitive control network (CCN) has been posited in the pathophysiology of LLD with EFD.

METHODS

Seventeen older adults with depression and EFD were randomized to iTBS or sham for 6 weeks. Intervention was delivered bilaterally using a recognized connectivity target.

RESULTS

A total of 89% (17/19) participants completed all study procedures. No serious adverse events occurred. Pre to post-intervention change in mean Montgomery-Asberg-depression scores was not different between iTBS or sham, p = 0.33. No significant group-by-time interaction for Montgomery-Asberg Depression rating scale scores (F _{3, 44}  = 0.51; p = 0.67) was found. No significant differences were seen in the effects of time between the two groups on executive measures: Flanker scores (F _{1, 14}  = 0.02, p = 0.88), Dimensional-change-card-sort scores F _{1, 14}  = 0.25, p = 0.63, and working memory scores (F _{1, 14}  = 0.98, p = 0.34). The Group-by-time interaction effect for functional connectivity (FC) within the Fronto-parietal-network was not significant (F _{1, 14}  = 0.36, p = 0.56). No significant difference in the effect-of-time between the two groups was found on FC within the Cingulo-opercular-network (F _{1, 14}  = 0, p = 0.98).

CONCLUSION

Bilateral iTBS is feasible in LLD. Preliminary results are unsupportive of efficacy on depression, executive function or target engagement of the CCN. A future Randomized clinical trial requires a larger sample size with stratification of cognitive and executive variables and refinement in the target engagement.

A Prospective Evaluation of Infant Cerebellar-Cerebral Functional Connectivity in Relation to Behavioral Development in Autism Spectrum Disorder

Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder diagnosed based on social impairment, restricted interests, and repetitive behaviors. Contemporary theories posit that cerebellar pathology contributes causally to ASD by disrupting error-based learning (EBL) during infancy. The present study represents the first test of this theory in a prospective infant sample, with potential implications for ASD detection.

Methods

Data from the Infant Brain Imaging Study (n = 94, 68 male) were used to examine 6-month cerebellar functional connectivity magnetic resonance imaging in relation to later (12/24-month) ASD-associated behaviors and outcomes. Hypothesis-driven univariate analyses and machine learning-based predictive tests examined cerebellar-frontoparietal network (FPN; subserves error signaling in support of EBL) and cerebellar-default mode network (DMN; broadly implicated in ASD) connections. Cerebellar-FPN functional connectivity was used as a proxy for EBL, and cerebellar-DMN functional connectivity provided a comparative foil. Data-driven functional connectivity magnetic resonance imaging enrichment examined brain-wide behavioral associations, with post hoc tests of cerebellar connections.

Results

Cerebellar-FPN and cerebellar-DMN connections did not demonstrate associations with ASD. Functional connectivity magnetic resonance imaging enrichment identified 6-month correlates of later ASD-associated behaviors in networks of a priori interest (FPN, DMN), as well as in cingulo-opercular (also implicated in error signaling) and medial visual networks. Post hoc tests did not suggest a role for cerebellar connections.

Conclusions

We failed to identify cerebellar functional connectivity-based contributions to ASD. However, we observed prospective correlates of ASD-associated behaviors in networks that support EBL. Future studies may replicate and extend network-level positive results, and tests of the cerebellum may investigate brain-behavior associations at different developmental stages and/or using different neuroimaging modalities.

Effect of methylphenidate on functional controllability: a preliminary study in medication-naïve children with ADHD

Methylphenidate (MPH) is the recommended first-line treatment for attention-deficit/hyperactivity disorder (ADHD). While MPH's mechanism of action as a dopamine and noradrenaline transporter blocker is well known, how this translates to ADHD-related symptom mitigation is still unclear. As functional connectivity is reliably altered in ADHD, with recent literature indicating dysfunctional connectivity dynamics as well, one possible mechanism is through altering brain network dynamics. In a double-blind, placebo-controlled MPH crossover trial, 19 medication-naïve children with ADHD underwent two functional MRI scanning sessions (one on MPH and one on placebo) that included a resting state scan and two inhibitory control tasks; 27 typically developing (TD) children completed the same protocol without medication. Network control theory, which quantifies how brain activity reacts to system inputs based on underlying connectivity, was used to assess differences in average and modal functional controllability during rest and both tasks between TD children and children with ADHD (on and off MPH) and between children with ADHD on and off MPH. Children with ADHD on placebo exhibited higher average controllability and lower modal controllability of attention, reward, and somatomotor networks than TD children. Children with ADHD on MPH were statistically indistinguishable from TD children on almost all controllability metrics. These findings suggest that MPH may stabilize functional network dynamics in children with ADHD, both reducing reactivity of brain organization and making it easier to achieve brain states necessary for cognitively demanding tasks.

Elevated integration within the reward network underlies vulnerability to distress

Distress tolerance (DT), the capability to persist under negative circumstances, underlies a range of psychopathologies. It has been proposed that DT may originate from the activity and connectivity in diverse neural networks integrated by the reward system. To test this hypothesis, we examined the link between DT and integration and segregation in the reward network as derived from resting-state functional connectivity data. DT was measured in 147 participants from a large community sample using the Behavioral Indicator of Resiliency to Distress task. Prior to DT evaluation, participants underwent a resting-state functional magnetic resonance imaging scan. For each participant, we constructed a whole-brain functional connectivity network and calculated the degree of reward network integration and segregation based on the extent to which reward network nodes showed functional connections within and outside their network. We found that distress-intolerant participants demonstrated heightened reward network integration relative to the distress-tolerant participants. In addition, these differences in integration were higher relative to the rest of the brain and, more specifically, the somatomotor network, which has been implicated in impulsive behavior. These findings support the notion that increased integration in large-scale brain networks may constitute a risk for distress intolerance and its psychopathological correlates.

Cross-Scale Dynamicity of Entropy and Connectivity in the Sleeping Brain

Introduction: The concept of local sleep refers to the phenomenon of local brain activity that modifies neural networks during unresponsive global sleep. Such network rewiring may differ across spatial scales; however, the global and local alterations in brain systems remain elusive in human sleep. Materials and Methods: We examined cross-scale changes of brain networks in sleep. Functional magnetic resonance imaging data were acquired from 28 healthy participants during nocturnal sleep. We adopted both metrics of connectivity (functional connectivity [FC] and regional homogeneity [ReHo]) and complexity (multiscale entropy) to explore the global and local functionality of the neural assembly across nonrapid eye movement sleep stages. Results: Long-range FC decreased with sleep depth, whereas local ReHo peaked at the N2 stage and reached its lowest level at the N3 stage. Entropy exhibited a general decline at the local scale (Scale 1) as sleep deepened, whereas the coarse-scale entropy (Scale 3) was consistent across stages. Discussion: The negative correlation between Scale-1 entropy and ReHo reflects the enhanced signal regularity and synchronization in sleep, identifying the information exchange at the local scale. The N2 stage showed a distinctive pattern toward local information processing with scrambled long-distance information exchange, indicating a specific time window for network reorganization. Collectively, the multidimensional metrics indicated an imbalanced global-local relationship among brain functional networks across sleep-wake stages.

Covariance-based vs. correlation-based functional connectivity dissociates healthy aging from Alzheimer disease

Prior studies of aging and Alzheimer disease have evaluated resting state functional connectivity (FC) using either seed-based correlation (SBC) or independent component analysis (ICA), with a focus on particular functional systems. SBC and ICA both are insensitive to differences in signal amplitude. At the same time, accumulating evidence indicates that the amplitude of spontaneous BOLD signal fluctuations is physiologically meaningful. We systematically compared covariance-based FC, which is sensitive to amplitude, vs. correlation-based FC, which is not, in affected individuals and controls drawn from two cohorts of participants including autosomal dominant Alzheimer disease (ADAD), late onset Alzheimer disease (LOAD), and age-matched controls. Functional connectivity was computed over 222 regions of interest and group differences were evaluated in terms of components projected onto a space of lower dimension. Our principal observations are: (1) Aging is associated with global loss of resting state fMRI signal amplitude that is approximately uniform across resting state networks. (2) Thus, covariance FC measures decrease with age whereas correlation FC is relatively preserved in healthy aging. (3) In contrast, symptomatic ADAD and LOAD both lead to loss of spontaneous activity amplitude as well as severely degraded correlation structure. These results demonstrate a double dissociation between age vs. Alzheimer disease and the amplitude vs. correlation structure of resting state BOLD signals. Modeling results suggest that the AD-associated loss of correlation structure is attributable to a relative increase in the fraction of locally restricted as opposed to widely shared variance.

Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging

Previous literature has focused on predicting a diagnostic label from structural brain imaging. Since subtle changes in the brain precede a cognitive decline in healthy and pathological aging, our study predicts future decline as a continuous trajectory instead. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of future cognitive decline in healthy and pathological aging. Nonbrain data (demographics, clinical, and neuropsychological scores), structural MRI, and functional connectivity data from OASIS-3 (N = 662; age = 46-96 years) were entered into cross-validated multitarget random forest models to predict future cognitive decline (measured by CDR and MMSE), on average 5.8 years into the future. The analysis was preregistered, and all analysis code is publicly available. Combining non-brain with structural data improved the continuous prediction of future cognitive decline (best test-set performance: R2 = 0.42). Cognitive performance, daily functioning, and subcortical volume drove the performance of our model. Including functional connectivity did not improve predictive accuracy. In the future, the prognosis of age-related cognitive decline may enable earlier and more effective individualized cognitive, pharmacological, and behavioral interventions.

Associations of lifetime concussion history and repetitive head impact exposure with resting-state functional connectivity in former collegiate American football players: An NCAA 15-year follow-up study

The objective of this study was to examine associations of lifetime concussion history (CHx) and an advanced metric of lifetime repetitive head impact exposure with resting-state functional connectivity (rsFC) across the whole-brain and among large-scale functional networks (Default Mode; Dorsal Attention; and Frontoparietal Control) in former collegiate football players. Individuals who completed at least one year of varsity collegiate football were eligible to participate in this observational cohort study (n = 48; aged 36–41 years; 79.2% white/Caucasian; 12.5±4.4 years of football played; all men). Individuals were excluded if they reported history/suspicion of psychotic disorder with active symptoms, contraindications to participation in study procedures (e.g., MRI safety concern), or inability to travel. Each participant provided concussion and football playing histories. Self-reported concussion history was analyzed in two different ways based on prior research: dichotomous “High” (≥3 concussions; n = 28) versus “Low” (<3 concussions; n = 20); and four ordinal categories (0–1 concussion [n = 19]; 2–4 concussions [n = 8]; 5–7 concussions [n = 9]; and ≥8 concussions [n = 12]). The Head Impact Exposure Estimate (HIEE) was calculated from football playing history captured via structured interview. Resting-state fMRI and T1-weighted MRI were acquired and preprocessed using established pipelines. Next, rsFC was calculated using the Seitzman et al., (2020) 300-ROI functional atlas. Whole-brain, within-network, and between-network rsFC were calculated using all ROIs and network-specific ROIs, respectively. Effects of CHx and HIEE on rsFC values were examined using separate multivariable linear regression models, with a-priori α set to 0.05. We observed no statistically significant associations between rsFC outcomes and either CHx or HIEE (ps ≥ .12). Neither CHx nor HIEE were associated with neural signatures that have been observed in studies of typical and pathological aging. While CHx and repetitive head impacts have been associated with changes in brain health in older former athletes, our preliminary results suggest that associations with rsFC may not be present in early midlife former football players.

Infant Visual Brain Development and Inherited Genetic Liability in Autism

OBJECTIVE

Autism spectrum disorder (ASD) is heritable, and younger siblings of ASD probands are at higher likelihood of developing ASD themselves. Prospective MRI studies of siblings report that atypical brain development precedes ASD diagnosis, although the link between brain maturation and genetic factors is unclear. Given that familial recurrence of ASD is predicted by higher levels of ASD traits in the proband, the authors investigated associations between proband ASD traits and brain development among younger siblings.

METHODS

In a sample of 384 proband-sibling pairs (89 pairs concordant for ASD), the authors examined associations between proband ASD traits and sibling brain development at 6, 12, and 24 months in key MRI phenotypes: total cerebral volume, cortical surface area, extra-axial cerebrospinal fluid, occipital cortical surface area, and splenium white matter microstructure. Results from primary analyses led the authors to implement a data-driven approach using functional connectivity MRI at 6 months.

RESULTS

Greater levels of proband ASD traits were associated with larger total cerebral volume and surface area and larger surface area and reduced white matter integrity in components of the visual system in siblings who developed ASD. This aligned with weaker functional connectivity between several networks and the visual system among all siblings during infancy.

CONCLUSIONS

The findings provide evidence that specific early brain MRI phenotypes of ASD reflect quantitative variation in familial ASD traits. Multimodal anatomical and functional convergence on cortical regions, fiber pathways, and functional networks involved in visual processing suggest that inherited liability has a role in shaping the prodromal development of visual circuitry in ASD.

Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease

"Brain-predicted age" quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18-89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.

Dataset of brain functional connectome and its maturation in adolescents

We provided the dataset of brain connectome matrices, their similarities measures to self and others longitudinally, and Kessler's psychological distress scales (K10) including the response to each question. The dataset can be used to replicate the results of the manuscript titled “A longitudinal study of functional connectome uniqueness and its association with psychological distress in adolescence”. The functional connectome (whole-brain and 13 networks) matrices were calculated from the resting-state functional MRIs (rs-fMRIs). We collected rs-fMRI and Kessler's psychological distress scale (K10) in 77 adolescents longitudinally up to 9 times from 12 years of age every four months. After removal of data with excessive motion, 262 functional connectome matrices were provided with this paper. The 300 regions of interest (ROIs) were defined using the Greene lab brain atlas. The functional connectome matrices were calculated as correlations between time series from any pair of ROIs extracted from pre-processed fMRIs. This dataset could be potentially used to1.

Understand developmental changes in the functional brain connectivity,

2.

As a normal control database of functional connectome matrices,

3.

Develop and validate connectome and network-related analysing methods.