22 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:
These pubmed results were generated on 2017/03/16
PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.
Network Optimization of Functional Connectivity within Default Mode Network Regions to Detect Cognitive Decline.
IEEE Trans Neural Syst Rehabil Eng. 2017 Mar 07;:
Authors: Chaovalitwongse WA, Won D, Seref O, Borghesani P, Askren MK, Willis S, Grabowski T
The rapid aging of the world's population is causing an increase in the prevalence of cognitive decline and degenerative brain disease in the elderly. Current diagnoses of amnestic and nonamnestic Mild Cognitive Impairment (MCI), which may represent early stage Alzheimer's disease or related degenerative conditions, are based on clinical grounds. The recent emergence of advanced network analyses of functional Magnetic Resonance Imaging (fMRI) data taken at cognitive rest has provided insight that declining functional connectivity of the default mode network (DMN) may be correlated with neurological disorders, and particularly prodromal Alzheimer's disease. The goal of this paper is to develop a network analysis technique using fMRI data to characterize transition stages from healthy brain aging to cognitive decline. Previous studies primarily focused on internodal connectivity of the DMN and often assume functional homogeneity within each DMN region. In this paper, we develop a technique that focuses on identifying critical intra-nodal DMN connectivity by incorporating sparsity into connectivity modeling of the k-cardinality tree (KCT) problem. Most biological networks are efficient and formed by sparse connections, and the KCT can potentially reveal sparse connectivity patterns that are biologically informative. The KCT problem is NP-hard, and existing solution approaches are mostly heuristic. Mathematical formulations of the KCT problem in the literature are not compact and do not provide good solution bounds. This paper presents new KCT formulations and a fast heuristic approach to efficiently solve the KCT models for large DMN regions. The results in this study demonstrate that traditional fMRI group analysis on DMN regions cannot detect any statistically significant connectivity differences between normal aging and cognitively impaired subjects in DMN regions, and the proposed KCT approaches are more sensitive than the state-of-the-art regional homogeneity approach in detecting significant differences in both left and right medial temporal regions of the DMN.
PMID: 28287976 [PubMed - as supplied by publisher]
Similarities and differences of functional connectivity in drug-naïve, first-episode adolescent and young adult with major depressive disorder and schizophrenia.
Sci Rep. 2017 Mar 13;7:44316
Authors: Wei S, Womer F, Geng H, Jiang X, Zhou Q, Chang M, Zhou Y, Tang Y, Wang F
Major depressive disorder (MDD) and schizophrenia (SZ) are considered two distinct psychiatric disorders. Yet, they have considerable overlap in symptomatology and clinical features, particularly in the initial phases of illness. The amygdala and prefrontal cortex (PFC) appear to have critical roles in these disorders; however, abnormalities appear to manifest differently. In our study forty-nine drug-naïve, first-episode MDD, 45 drug-naïve, first-episode SZ, and 50 healthy control (HC) participants from 13 to 30 years old underwent resting-state functional magnetic resonance imaging. Functional connectivity (FC) between the amygdala and PFC was compared among the three groups. Significant differences in FC were observed between the amygdala and ventral PFC (VPFC), dorsolateral PFC (DLPFC), and dorsal anterior cingulated cortex (dACC) among the three groups. Further analyses demonstrated that MDD showed decreased amygdala-VPFC FC and SZ had reductions in amygdala-dACC FC. Both the diagnostic groups had significantly decreased amygdala-DLPFC FC. These indicate abnormalities in amygdala-PFC FC and further support the importance of the interaction between the amygdala and PFC in adolescents and young adults with these disorders. Additionally, the alterations in amygdala-PFC FC may underlie the initial similarities observed between MDD and SZ and suggest potential markers of differentiation between the disorders at first onset.
PMID: 28287187 [PubMed - in process]
Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM.
J Neurosci Methods. 2017 Mar 09;:
Authors: Hojjati SH, Ebrahimzadeh A, Khazaee A, Babajani-Feremi A
BACKGROUND: We investigated identifying patients with mild cognitive impairment (MCI) who progress to Alzheimer's disease (AD), MCI converter (MCI-C), from those with MCI who do not progress to AD, MCI non-converter (MCI-NC), based on resting-state fMRI (rs-fMRI).
NEW METHOD: Graph theory and machine learning approach were utilized to predict progress of patients with MCI to AD using rs-fMRI. Eighteen MCI converts (average age 73.6 years; 11 male) and 62 age-matched MCI non-converters (average age 73.0 years, 28 male) were included in this study. We trained and tested a support vector machine (SVM) to classify MCI-C from MCI-NC using features constructed based on the local and global graph measures. A novel feature selection algorithm was developed and utilized to select an optimal subset of features.
RESULTS: Using subset of optimal features in SVM, we classified MCI-C from MCI-NC with an accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve of 91.4%, 83.24%, 90.1%, and 0.95, respectively. Furthermore, results of our statistical analyses were used to identify the affected brain regions in AD.
COMPARISON WITH EXISTING METHOD(S): To the best of our knowledge, this is the first study that combines the graph measures (constructed based on rs-fMRI) with machine learning approach and accurately classify MCI-C from MCI-NC.
CONCLUSION: Results of this study demonstrate potential of the proposed approach for early AD diagnosis and demonstrate capability of rs-fMRI to predict conversion from MCI to AD by identifying affected brain regions underlying this conversion.
PMID: 28286064 [PubMed - as supplied by publisher]
Development of rostral inferior parietal lobule area functional connectivity from late childhood to early adulthood.
Int J Dev Neurosci. 2017 Mar 07;:
Authors: Wang M, Zhang J, Dong G, Zhang H, Lu H, Du X
Although the mirror neuron system (MNS) has been extensively studied in monkeys and adult humans, very little is known about its development. Previous studies suggest that the MNS is present by infancy and that the brain and MNS-related cognitive abilities (such as language, empathy, and imitation learning) continue to develop after childhood. In humans, the PFt area of the inferior parietal lobule (IPL) seems to particularly correlate with the functional properties of the PF area in primates, which contains mirror neurons. However, little is known about the functional connectivity (FC) of the PFt area with other brain areas and whether these networks change over time. Here, we investigated the FC development of the PFt area-based network in 61 healthy subjects aged 7-26 years at resting-state to study brain development from late childhood through adolescence to early adulthood. The bilateral PFt showed similar core FC networks, which included the frontal lobe, the cingulate gyri, the insula, the somatosensory cortex, the precuneus, the superior and inferior parietal lobules, the temporal lobe, and the cerebellum posterior lobes. Furthermore, the FC between the left PFt and the left IPL exhibited a significantly positive correlation with age, and the FC between the left PFt and the right postcentral gyrus exhibited a significantly negative correlation with age. In addition, the FC between the right PFt and the right putamen exhibited a significantly negative correlation with age. Our findings suggest that the PFt area-based network develops and is reorganized with age.
PMID: 28285946 [PubMed - as supplied by publisher]
Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies.
Neurosci Bull. 2017 Mar 10;:
Authors: Li D, Karnath HO, Xu X
Searching for effective biomarkers is one of the most challenging tasks in the research field of Autism Spectrum Disorder (ASD). Magnetic resonance imaging (MRI) provides a non-invasive and powerful tool for investigating changes in the structure, function, maturation, connectivity, and metabolism of the brain of children with ASD. Here, we review the more recent MRI studies in young children with ASD, aiming to provide candidate biomarkers for the diagnosis of childhood ASD. The review covers structural imaging methods, diffusion tensor imaging, resting-state functional MRI, and magnetic resonance spectroscopy. Future advances in neuroimaging techniques, as well as cross-disciplinary studies and large-scale collaborations will be needed for an integrated approach linking neuroimaging, genetics, and phenotypic data to allow the discovery of new, effective biomarkers.
PMID: 28283808 [PubMed - as supplied by publisher]
Use of resting-state fMRI in planning epilepsy surgery.
Neurol India. 2017;65(Supplement):S25-S33
Authors: Chiang S, Haneef Z, Stern JM, Engel J
Epileptic seizures result from abnormal neuronal excitability and synchronization, affecting 0.5-1% of the population worldwide. Although anti-seizure drugs are often effective, a significant number of patients with epilepsy continue to experience refractory seizures and are candidates for surgical resection. Whereas standard presurgical evaluation has relied on intracranial electroencephalography (icEEG) and direct cortical stimulation to identify epileptogenic tissue and areas of cortex for which resection would produce clinical deficits, the invasive nature and limited spatial extent of icEEG has led to the investigation of less invasive imaging modalities as adjunctive tools in the presurgical workup. In the past few decades, functional connectivity MRI has emerged as a promising approach for presurgical mapping, leading to a surge in the number of proposed methods and biomarkers for identifying epileptogenic tissue. This review focuses on recent advances in the use of functional connectivity MRI toward its application for presurgical planning, including epilepsy localization and eloquent cortex mapping.
PMID: 28281493 [PubMed - in process]
At risk of being risky: The relationship between "brain age" under emotional states and risk preference.
Dev Cogn Neurosci. 2017 Feb 01;24:93-106
Authors: Rudolph MD, Miranda-Domínguez O, Cohen AO, Breiner K, Steinberg L, Bonnie RJ, Scott ES, Taylor-Thompson K, Chein J, Fettich KC, Richeson JA, Dellarco DV, Galván A, Casey BJ, Fair DA
Developmental differences regarding decision making are often reported in the absence of emotional stimuli and without context, failing to explain why some individuals are more likely to have a greater inclination toward risk. The current study (N=212; 10-25y) examined the influence of emotional context on underlying functional brain connectivity over development and its impact on risk preference. Using functional imaging data in a neutral brain-state we first identify the "brain age" of a given individual then validate it with an independent measure of cortical thickness. We then show, on average, that "brain age" across the group during the teen years has the propensity to look younger in emotional contexts. Further, we show this phenotype (i.e. a younger brain age in emotional contexts) relates to a group mean difference in risk perception - a pattern exemplified greatest in young-adults (ages 18-21). The results are suggestive of a specified functional brain phenotype that relates to being at "risk to be risky."
PMID: 28279917 [PubMed - as supplied by publisher]
Mnemonic Training Reshapes Brain Networks to Support Superior Memory.
Neuron. 2017 Mar 08;93(5):1227-1235.e6
Authors: Dresler M, Shirer WR, Konrad BN, Müller NC, Wagner IC, Fernández G, Czisch M, Greicius MD
Memory skills strongly differ across the general population; however, little is known about the brain characteristics supporting superior memory performance. Here we assess functional brain network organization of 23 of the world's most successful memory athletes and matched controls with fMRI during both task-free resting state baseline and active memory encoding. We demonstrate that, in a group of naive controls, functional connectivity changes induced by 6 weeks of mnemonic training were correlated with the network organization that distinguishes athletes from controls. During rest, this effect was mainly driven by connections between rather than within the visual, medial temporal lobe and default mode networks, whereas during task it was driven by connectivity within these networks. Similarity with memory athlete connectivity patterns predicted memory improvements up to 4 months after training. In conclusion, mnemonic training drives distributed rather than regional changes, reorganizing the brain's functional network organization to enable superior memory performance.
PMID: 28279356 [PubMed - in process]
Parallel group independent component analysis for massive fMRI data sets.
PLoS One. 2017;12(3):e0173496
Authors: Chen S, Huang L, Qiu H, Nebel MB, Mostofsky SH, Pekar JJ, Lindquist MA, Eloyan A, Caffo BS
Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively.
PMID: 28278208 [PubMed - in process]
Resting-State BOLD MRI for Perfusion and Ischemia.
Top Magn Reson Imaging. 2017 Mar 08;:
Authors: Kroll H, Zaharchuk G, Christen T, Heit JJ, Iv M
Advanced imaging techniques including computed tomography (CT) angiography, CT perfusion, magnetic resonance (MR) angiography, MR with diffusion- and perfusion-weighted imaging, and, more recently, resting-state BOLD (Blood Oxygen Level Dependent) functional MRI (rs-fMRI) are increasingly used to evaluate patients with acute ischemic stroke. Advanced imaging allows for identification of patients with ischemic stroke and determination of the size of infarcted and potentially salvageable tissue, all of which yield crucial information for proper stroke management. The addition of rs-fMRI for ischemia adds information at the microvascular level, thereby improving the understanding of pathophysiologic mechanisms of impaired cerebral perfusion and tissue oxygenation beyond the known concepts at the macrovascular level. As such, it may further delineate functional and dysfunctional neuronal networks, guide stroke interventions, and improve prognosis and monitoring of patient outcomes.
PMID: 28277456 [PubMed - as supplied by publisher]
Development of White Matter Microstructure and Intrinsic Functional Connectivity Between the Amygdala and Ventromedial Prefrontal Cortex: Associations With Anxiety and Depression.
Biol Psychiatry. 2017 Jan 17;:
Authors: Jalbrzikowski M, Larsen B, Hallquist MN, Foran W, Calabro F, Luna B
BACKGROUND: Connectivity between the amygdala and ventromedial prefrontal cortex (vmPFC) is compromised in multiple psychiatric disorders, many of which emerge during adolescence. To identify to what extent the deviations in amygdala-vmPFC maturation contribute to the onset of psychiatric disorders, it is essential to characterize amygdala-vmPFC connectivity changes during typical development.
METHODS: Using an accelerated cohort longitudinal design (1-3 time points, 10-25 years old, n = 246), we characterized developmental changes of the amygdala-vmPFC subregion functional and structural connectivity using resting-state functional magnetic resonance imaging and diffusion-weighted imaging.
RESULTS: Functional connectivity between the centromedial amygdala and rostral anterior cingulate cortex (rACC), anterior vmPFC, and subgenual cingulate significantly decreased from late childhood to early adulthood in male and female subjects. Age-associated decreases were also observed between the basolateral amygdala and the rACC. Importantly, these findings were replicated in a separate cohort (10-22 years old, n = 327). Similarly, structural connectivity, as measured by quantitative anisotropy, significantly decreased with age in the same regions. Functional connectivity between the centromedial amygdala and the rACC was associated with structural connectivity in these same regions during early adulthood (22-25 years old). Finally, a novel time-varying coefficient analysis showed that increased centromedial amygdala-rACC functional connectivity was associated with greater anxiety and depression symptoms during early adulthood, while increased structural connectivity in centromedial amygdala-anterior vmPFC white matter was associated with greater anxiety/depression during late childhood.
CONCLUSIONS: Specific developmental periods of functional and structural connectivity between the amygdala and the prefrontal systems may contribute to the emergence of anxiety and depressive symptoms and may play a critical role in the emergence of psychiatric disorders in adolescence.
PMID: 28274468 [PubMed - as supplied by publisher]
Resting State Effective Connectivity Allows Auditory Hallucination Discrimination.
Int J Neural Syst. 2017 Feb 01;:1750019
Authors: Graña M, Ozaeta L, Chyzhyk D
Hallucinations are elusive phenomena that have been associated with psychotic behavior, but that have a high prevalence in healthy population. Some generative mechanisms of Auditory Hallucinations (AH) have been proposed in the literature, but so far empirical evidence is scarce. The most widely accepted generative mechanism hypothesis nowadays consists in the faulty workings of a network of brain areas including the emotional control, the audio and language processing, and the inhibition and self-attribution of the signals in the auditive cortex. In this paper, we consider two methods to analyze resting state fMRI (rs-fMRI) data, in order to measure effective connections between the brain regions involved in the AH generation process. These measures are the Dynamic Causal Modeling (DCM) cross-covariance function (CCF) coefficients, and the partially directed coherence (PDC) coefficients derived from Granger Causality (GC) analysis. Effective connectivity measures are treated as input classifier features to assess their significance by means of cross-validation classification accuracy results in a wrapper feature selection approach. Experimental results using Support Vector Machine (SVM) classifiers on an rs-fMRI dataset of schizophrenia patients with and without a history of AH confirm that the main regions identified in the AH generative mechanism hypothesis have significant effective connection values, under both DCM and PDC evaluation.
PMID: 28274168 [PubMed - as supplied by publisher]
The influence of posterior visual pathway damage on visual information processing speed in multiple sclerosis.
Mult Scler. 2016 Nov 01;:1352458516676642
Authors: Gabilondo I, Rilo O, Ojeda N, Pena J, Gómez-Gastiasoro A, Mendibe Bilbao M, Rodríguez-Antigüedad A, Cabrera A, Diez I, Ibarretxe-Bilbao N
BACKGROUND: The injury of visual pathway and abnormalities of visual processing speed (VPS) are frequent in MS, but their association remains unexplored.
OBJECTIVE: To evaluate the impact of posterior visual pathway structural and functional integrity on VPS of MS patients.
METHODS: Cross-sectional study of 30 MS patients and 28 controls, evaluating the association of a VPS tests composite (Salthouse Perceptual Comparison test, Trail Making Test A and Symbol Digit Modalities Test) with 3T MRI visual cortex thickness, optic radiations (OR) diffusion tensor imaging indexes, and medial visual component (MVC) functional connectivity (FC) (MVC-MVC FC (iFC) and MVC-brain FC (eFC)) by linear regression, removing the effect of premorbid IQ, fatigue, and depression.
RESULTS: V2 atrophy, lower OR fractional anisotropy (FA) and MVC FC significantly influenced VPS in MS (at none or lesser extent in controls), even after removing the effect of Expanded Disability Status Scale and previous optic neuritis (V2 ( r(2) = 0.210): β = +0.366, p = 0.046; OR FA ( r(2) = 0.243): β = +0.378, p = 0.034; MVC iFC, for example, left cuneus ( r(2) = 0.450): β = -0.613, p < 0.001; MVC eFC, for example, right precuneus-postcentral gyrus ( r(2) = 0.368): β = -0.466, p = 0.002).
CONCLUSION: Posterior visual pathway integrity, structural (V2 thickness and OR FA) and functional (MVC FC), may explain respectively up to 24% and 45% of VPS variability in MS.
PMID: 28273763 [PubMed - as supplied by publisher]
18 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:
These pubmed results were generated on 2017/03/09
PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.
Task and resting-state fMRI studies in first-episode schizophrenia: A systematic review.
Schizophr Res. 2017 Mar 03;:
Authors: Mwansisya TE, Hu A, Li Y, Chen X, Wu G, Huang X, Lv D, Li Z, Liu C, Xue Z, Feng J, Liu Z
In the last two decades there has been an increase on task and resting-state functional Magnetic Resonance Imaging (fMRI) studies that explore the brain's functional changes in schizophrenia. However, it remains unclear as to whether the brain's functional changes during the resting state are sensitive to the same brain regions during task fMRI. Therefore, we conducted a systematic literature search of task and resting-state fMRI studies that investigated brain pathological changes in first-episode schizophrenia (Fleischhacker et al.). Nineteen studies met the inclusion criteria; seven were resting state fMRI studies with 371 FES patients and 363 healthy controls and twelve were task fMRI studies with 235 FES patients and 291 healthy controls. We found overlapping task and resting-state fMRI abnormalities in the prefrontal regions, including the dorsal lateral prefrontal cortex, the orbital frontal cortex and the temporal lobe, especially in the left superior temporal gyrus (STG). The findings of this systematic review support the frontotemporal hypothesis of schizophrenia, and the disruption in prefrontal and STG might represent the pathophysiology of schizophrenia disorder at a relatively early stage.
PMID: 28268041 [PubMed - as supplied by publisher]
Bayesian Switching Factor Analysis for Estimating Time-varying Functional Connectivity in fMRI.
Neuroimage. 2017 Mar 03;:
Authors: Taghia J, Ryali S, Chen T, Supekar K, Cai W, Menon V
There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000). We refer to this model as Bayesian switching factor analysis (BSFA) as it integrates factor analysis into a generative HMM in a unified Bayesian framework. In BSFA, brain dynamic functional networks are represented by latent states which are learnt from the data. Crucially, BSFA is a generative model which estimates the temporal evolution of brain states and transition probabilities between states as a function of time. An attractive feature of BSFA is the automatic determination of the number of latent states via Bayesian model selection arising from penalization of excessively complex models. Key features of BSFA are validated using extensive simulations on carefully designed synthetic data. We further validate BSFA using fingerprint analysis of multisession resting-state fMRI data from the Human Connectome Project (HCP). Our results show that modeling temporal dependencies in the generative model of BSFA results in improved fingerprinting of individual participants. Finally, we apply BSFA to elucidate the dynamic functional organization of the salience, central-executive, and default mode networks-three core neurocognitive systems with central role in cognitive and affective information processing (Menon, 2011). Across two HCP sessions, we demonstrate a high level of dynamic interactions between these networks and determine that the salience network has the highest temporal flexibility among the three networks. Our proposed methods provide a novel and powerful generative model for investigating dynamic brain connectivity.
PMID: 28267626 [PubMed - as supplied by publisher]
Ensuring that novel resting-state fMRI metrics are physiologically grounded, interpretable, and meaningful (A commentary on Canna et al., 2017).
Eur J Neurosci. 2017 Mar 07;:
Authors: Dunlop K, Downar J
PMID: 28267225 [PubMed - as supplied by publisher]
Alteration of Spontaneous Brain Activity After Hypoxia-Reoxygenation: A Resting-State fMRI Study.
High Alt Med Biol. 2017 Mar 07;:
Authors: Zhang J, Chen J, Fan C, Li J, Lin J, Yang T, Fan M
Zhang, Jiaxing, Ji Chen, Cunxiu Fan, Jinqiang Li, Jianzhong Lin, Tianhe Yang, and Ming Fan. Alteration of spontaneous brain activity after hypoxia-reoxygenation: A resting-state fMRI study. High Alt Med Biol. 18:000-000, 2017.-The present study was designed to investigate the effect of hypoxia-reoxygenation on the spontaneous neuronal activity in brain. Sixteen sea-level (SL) soldiers (20.5 ± 0.7 years), who garrisoned the frontiers in high altitude (HA) (2300-4400 m) for two years and subsequently descended to sea level for one to seven days, were recruited. Control group consisted of 16 matched SL natives. The amplitude of low-frequency fluctuations (ALFF) of regional brain functional magnetic resonance imaging signal in resting state and functional connectivity (FC) between brain regions was analyzed. HA subjects showed significant increases of ALFF at several sites within the bilateral occipital cortices and significant decreases of ALFF in the right anterior insula and extending to the caudate, putamen, inferior frontal orbital cortex, temporal pole, and superior temporal gyrus; lower ALFF values in the right insula were positively correlated with low respiratory measurements. The right insula in HA subjects had increases of FC with the right superior temporal gyrus, postcentral gyrus, rolandic operculum, supramarginal gyrus, and inferior frontal triangular area. We thus demonstrated that hypoxia-reoxygenation had influence on the spontaneous neuronal activity in brain. The decrease of insular neuronal activity may be related to the reduction of ventilatory drive, while the increase of FC with insula may indicate a central compensation.
PMID: 28266873 [PubMed - as supplied by publisher]
Intact sensory-motor network structure and function in far from onset premanifest Huntington's disease.
Sci Rep. 2017 Mar 07;7:43841
Authors: Gorges M, Müller HP, Mayer IM, Grupe GS, Kammer T, Grön G, Kassubek J, Landwehrmeyer GB, Wolf RC, Orth M
Structural and functional changes attributable to the neurodegenerative process in Huntington's disease (HD) may be evident in HTT CAG repeat expansion carriers before the clinical manifestations of HD. It remains unclear, though, how far from motor onset a consistent signature of the neurodegenerative process in HD can be detected. Twelve far from onset preHD and 22 age-matched healthy control participants underwent volumetric structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and resting-state functional MRI (11 preHD, 22 controls) as well as electrophysiological measurements (12 preHD, 13 controls). There were no significant differences in white matter macro- and microstructure between far from onset preHD participants and controls. Functional connectivity in a basal ganglia-thalamic and motor networks, all measures of the motor efferent and sensory afferent pathways as well as sensory-motor integration were also similar in far from onset preHD and controls. With the methods used in far from onset preHD sensory-motor neural macro- or micro-structure and brain function were similar to healthy controls. This suggests that any observable structural and functional change in preHD nearer to onset, or in manifest HD, at least using comparable techniques such as in this study, most likely reflects an ongoing neurodegenerative process.
PMID: 28266655 [PubMed - in process]