Environmental and social changes during early school age have a profound impact on brain development. However, it remains unclear how the brains of typically-developing children adjust white matter to optimize network topology during this period. This study aims to propose the fiber length distribution as a novel nodal metric to capture the continuous maturation of brain network. We scanned dMRI among N=30 typically-developing children in their first year of primary school and one-year follow up. We assessed the longitudinal changes in fiber length distribution, characterized by the median length of connected fibers for each brain region. The length median was positively correlated with degree and betweenness centrality, while negatively correlated with clustering coefficient and local efficiency. From ages 7 to 8, we observed significant decreases in length median in the temporal, superior parietal, anterior cingulate, and medial prefrontal cortex, accompanied by a reduction in long-range connections and an increase in short-range connections. Meta-analytic decoding revealed that the widespread decrease in length median occurred in regions responsible for sensory processing, whereas a more localized increase in length median was observed in regions involved in memory and cognitive control. Finally, simulations test on healthy adults further support that the decrease in long-range and increase in short-range connection contributed to increased network segregation and integration, respectively. Our results suggest that the dual process of short- and long-range fiber changes reflects a cost-efficient strategy for optimizing network organization during this critical developmental stage.
Abstract Effective connectivity measurements in the human hippocampal memory system based on the resting-state blood oxygenation-level dependent signal were made in 172 participants in the Human Connectome Project to reveal the directionality and strength of the connectivity. A ventral “what” hippocampal stream involves the temporal lobe cortex, perirhinal and parahippocampal TF cortex, and entorhinal cortex. A dorsal “where” hippocampal stream connects parietal cortex with posterior and retrosplenial cingulate cortex, and with parahippocampal TH cortex, which, in turn, project to the presubiculum, which connects to the hippocampus. A third stream involves the orbitofrontal and ventromedial-prefrontal cortex with effective connectivity with the hippocampal, entorhinal, and perirhinal cortex. There is generally stronger forward connectivity to the hippocampus than backward. Thus separate “what,” “where,” and “reward” streams can converge in the hippocampus, from which back projections return to the sources. However, unlike the simple dual stream hippocampal model, there is a third stream related to reward value; there is some cross-connectivity between these systems before the hippocampus is reached; and the hippocampus has some effective connectivity with earlier stages of processing than the entorhinal cortex and presubiculum. These findings complement diffusion tractography and provide a foundation for new concepts on the operation of the human hippocampal memory system.
Methadone maintenance treatment (MMT) has elevated rates of co-morbid memory deficit and depression that are associated with higher relapse rates for substance abuse. White matter (WM) disruption in MMT patients have been reported but their impact on these co-morbidities is unknown. This study aimed to investigate changes in WM integrity of MMT subjects using diffusion tensor image (DTI), and their relationship with history of heroin and methadone use in treated opiate-dependent individuals. The association between WM integrity changes from direct group comparisons and the severity of memory deficit and depression was also investigated. Differences in WM integrity between 35 MMT patients and 23 healthy controls were evaluated using DTI with tract-based spatial statistical analysis. Differences in DTI indices correlated with diminished memory function, Beck Depression Inventory, duration of heroin use and MMT, and dose of heroin and methadone administration. Changes in WM integrity were found in several WM regions, including the temporal and frontal lobes, pons, cerebellum, and cingulum bundles. The duration of MMT was associated with declining DTI indices in the superior longitudinal fasciculus and para-hippocampus. MMT patients had more memory and emotional deficits than healthy subjects. Worse scores in both depression and memory functions were associated with altered WM integrity in the superior longitudinal fasciculus, para-hippocampus, and middle cerebellar peduncle in MMT. Patients on MMT also had significant WM differences in the reward circuit and in depression- and memory-associated regions. Correlations among decreased DTI indices, disease severity, and accumulation effects of methadone suggest that WM alterations may be involved in the psychopathology and pathophysiology of co-morbidities in MMT.
Previous functional imaging studies in episodic cluster headache (CH) patients revealed altered brain metabolism concentrated on the central descending pain control system. However, it remains unclear whether changes in brain metabolism during the "in bout" period are due to structural changes and whether these structural changes vary between the "in bout" and "out of bout" periods. To quantify brain structural changes in CH patients, the regional gray matter volume (GMV) was compared among 49 episodic CH patients during the "in bout" period and 49 age- and sex-matched controls. Twelve patients were rescanned during the "out of bout" period to evaluate the changes, if any, between these 2 periods. Compared with healthy controls, CH patients showed significant "in bout" GMV reductions in the bilateral middle frontal, left superior, and medial frontal gyri. Compared to "out of bout" scans, the "in bout" scans revealed significant GMV increases in the left anterior cingulate, insula, and fusiform gyrus. Additionally, compared to healthy controls, the "out of bout" scans revealed a trend of GMV reduction in the left middle frontal gyrus. These affected regions primarily belong to frontal pain modulation areas, and thus these GMV changes may reflect insufficient pain-modulating capacity in the frontal areas of CH patients.
Accumulating evidence showed that major depressive disorder (MDD) is characterized by a dysfunction of serotonin neurotransmission. Raphe nuclei are the sources of most serotonergic neurons that project throughout the brain. Incorporating measurements of activity within the raphe nuclei into the analysis of connectivity characteristics may contribute to understanding how neurotransmitter synthesized centers are involved in the pathogenesis of MDD. Here, we analyzed the resting-state functional magnetic resonance imaging (RS-fMRI) dataset from 1,148 MDD patients and 1,079 healthy individuals recruited across nine centers. A seed-based analysis with the dorsal raphe and median raphe nuclei was performed to explore the functional connectivity (FC) alterations. Compared to controls, for dorsal raphe, the significantly decreased FC linking with the right precuneus and median cingulate cortex were found; for median raphe, the increased FC linking with right superior cerebellum (lobules V/VI) was found in MDD patients. In further exploratory analyzes, MDD-related connectivity alterations in dorsal and median raphe nuclei in different clinical factors remained highly similar to the main findings, indicating these abnormal connectivities are a disease-related alteration. Our study highlights a functional dysconnection pattern of raphe nuclei in MDD with multi-site big data. These findings help improve our understanding of the pathophysiology of depression and provide evidence of the theoretical foundation for the development of novel pharmacotherapies.
Motivation: Preclinical stages of AD offer potential windows for intervention. Investigating individuals in this stages can yield valuable biomarkers and deepen our understanding of disease progression mechanisms. Goal(s): We aim to investigate brain degeneration mechanisms during AD's preclinical stages and explore early diagnostic markers in gray matter and superficial white matter alterations. Approach: This study involved 411 participants (including preclinal stages, aMCI and AD) and their diffusion and structural MRI and neuropsychological tests, to assess brain changes. Results: Cortical atrophy in the temporal lobe may be a trigger for disease onset, while extensive SWM degeneration appears to be associated with disease progression in AD. Impact: This study provides crucial insights into brain changes in early stages of AD. Identified imaging biomarkers are valuable for early diagnosis and interventions, and the proposed degeneration patterns enhance our understanding of AD's pathophysiology.
After the outbreak of COVID-19, we conducted a DTI-related research to explore the cerebral micro-structural changes in recovered COVID-19 patients. According to our results, we supposed that the regional volumetric enlargement was caused by neurogenesis and hypertrophy, and remyelination possibly existed in the white matter pathways of these patients [[1]Lu Y. Li X. Geng D. et al.Cerebral micro-structural changes in COVID-19 Patients – an MRI-based 3-month follow-up study.EClinicalMedicine. 2020; 25100484Summary Full Text Full Text PDF Scopus (294) Google Scholar]. E. Goldberg, et al. proposed persistent neuroinflammation as an alternative explanation. In our study, the COVID-19 and non-COVID-19 patients were matched for age and sex. The patients with premorbid neurocognitive diseases were excluded at the beginning. Accordingly, no volunteers with such conditions were recruited. Thus, the impact of underlying neurocognitive conditions was ruled out. We agree that the result could be more convincing if the GMV (grey matter volume)-related factors including education, physical activity and BMI, were matched or adjusted [[2]Foubert-Samier A. Catheline G. Amieva H. et al.Education, occupation, leisure activities, and brain reserve: a population-based study.Neurobiol Aging. 2012; 33 (423.e15-423.e25)Crossref PubMed Scopus (173) Google Scholar,[3]Gogniat M.A. Robinson T.L. Mewborn C.M. Jean K.R. Miller L.S Body mass index and its relation to neuropsychological functioning and brain volume in healthy older adults.Behav Brain Res. 2018; 348: 235-240Crossref PubMed Scopus (11) Google Scholar]. The regional increased GMV and decreased MD/AD values could result from neuroinflammation in the acute or chronic stage. The inflammatory response to neurotropic coronavirus infection was reported to reach peak within the first month and could last for 2–3 months [[4]Mori S. Zhang J. Principles of diffusion tensor imaging and its applications to basic neuroscience research.Neuron. 2006; 51: 527-539Summary Full Text Full Text PDF PubMed Scopus (1316) Google Scholar]. Concerning the fact that the patients in our study were collected 3 months after recovery (twice PCR negative tests, mean duration from the onset to the date of MRI scans, 97.46±8.01 days), we thought it was hard to attribute the GMV enlargement to persistent inflammation. However, our sample size was quite limited. It is encouraged to explore the dynamic cerebral changes in COVID-19 patients and their relationship with neurological manifestations and cognitive behaviors in the future with larger samples. The authors have nothing to declare. The brain after COVID-19: Compensatory neurogenesis or persistent neuroinflammation?Yiping Lu et al. [1] report increased grey matter volumes and changes in MRI-based measures of water diffusion in white matter in the brains of recovered COVID-19 patients three months after acute illness, compared to healthy controls. They propose that neurogenesis and hypertrophy caused volumetric enlargement, and pathway remyelination restricted diffusion in the patients. If valid, these explanations suggest vigorous and counter-intuitive compensatory brain mechanisms during recovery from COVID-19. Full-Text PDF Open AccessCerebral Micro-Structural Changes in COVID-19 Patients – An MRI-based 3-month Follow-up StudyStudy findings revealed possible disruption to micro-structural and functional brain integrity in the recovery stages of COVID-19, suggesting the long-term consequences of SARS-CoV-2. Full-Text PDF Open Access
Significance Using network analysis of resting-state functional MRI data, we demonstrate that significant randomization of global network metrics, and greater resilience to targeted attack on network hubs, was replicably demonstrable in Chinese patients with schizophrenia, and was also demonstrated for the first time in their nonpsychotic first-degree relatives. These results support the hypothesis that functional networks are abnormally randomized and resilient in schizophrenia and indicate that network randomization/resilience may be an endophenotype, or marker of familial risk, for schizophrenia. We suggest that the greater randomization of the brain network endophenotype of schizophrenia may confer advantages in terms of greater resilience to pathological attack that may explain the selection and persistence of risk genes for schizophrenia in the general population.
Abstract The neurobiological heterogeneity of schizophrenia is widely accepted, but it is unclear how mechanistic differences converge to produce the observed phenotype. Establishing a pathophysiological model that accounts for both heterogeneity and phenotypic similarity is essential to inform stratified treatment approaches. In this cross-sectional diffusion tensor imaging (DTI) study, we recruited 77 healthy controls (HC), and 71 patients with DSM-IV diagnosis of schizophrenia (SCZ), and reconstructed the structural connectivity of 90 brain regions covering entire cerebral cortex. We first confirmed the heterogeneity in structural connectivity by showing a reduced inter-individual similarity in SCZ compared with HC. Moreover, we found it was not possible to cluster patients into subgroups with shared patterns of dysconnectivity, indicating a high degree of mechanistic divergence in schizophrenia. Instead of the strength of connectivity between any particular brain regions, we investigated the diversity (or statistically, the variance) of the topographic distribution of the strength was reduced. HC had higher topographic diversity in whole brain structural connectivity compared to the patient group (P = 2 × 10 −6 , T = 4.96, Cohen ′ S d = 0.87). In 62 of the 90 brain regions, the topographic diversity was significantly reduced in patients compared to controls after FDR correction (<0.05). When topographic diversity was used as a discriminant feature for classification between patients and controls, we significantly (P = 4.29 × 10 −24 ) improved the classification accuracy to 79.6% (sensitivity 78.3%, specificity 81.3%). This finding suggests highly individualized pattern of structural dysconnectivity underlying the heterogeneity of schizophrenia converges to a convergent common pathway as reduced topographic diversity for the clinical construct of the disease.
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