57,769 research outputs found
TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy
We introduce TempoCave, a novel visualization application for analyzing
dynamic brain networks, or connectomes. TempoCave provides a range of
functionality to explore metrics related to the activity patterns and modular
affiliations of different regions in the brain. These patterns are calculated
by processing raw data retrieved functional magnetic resonance imaging (fMRI)
scans, which creates a network of weighted edges between each brain region,
where the weight indicates how likely these regions are to activate
synchronously. In particular, we support the analysis needs of clinical
psychologists, who examine these modular affiliations and weighted edges and
their temporal dynamics, utilizing them to understand relationships between
neurological disorders and brain activity, which could have a significant
impact on the way in which patients are diagnosed and treated. We summarize the
core functionality of TempoCave, which supports a range of comparative tasks,
and runs both in a desktop mode and in an immersive mode. Furthermore, we
present a real-world use case that analyzes pre- and post-treatment connectome
datasets from 27 subjects in a clinical study investigating the use of
cognitive behavior therapy to treat major depression disorder, indicating that
TempoCave can provide new insight into the dynamic behavior of the human brain
Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach
Progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and idiopathic Parkinsonās disease (IPD) can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs). An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i) a subcortical motor network; (ii) each of its component regions and (iii) the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process
Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability
Postural Instability (PI) is a core feature of
Parkinsonās Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method.
To evaluate gait performance, spatial-temporal (S-T) gait
parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy
Brain networks under attack : robustness properties and the impact of lesions
A growing number of studies approach the brain as a complex network, the so-called āconnectomeā. Adopting this framework, we examine what types or extent of damage the brain can withstandāreferred to as network ārobustnessāāand conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimerās disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regionsāand especially those connecting different subnetworksāwas found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research
Resting state functional thalamic connectivity abnormalities in patients with post-stroke sleep apnoea: a pilot case-control study
OBJECTIVE: Sleep apnoea is common
after stroke, and has adverse effects on the
clinical outcome of affected cases. Its pathophysiological
mechanisms are only partially known. Increases
in brain connectivity after stroke might influence
networks involved in arousal modulation
and breathing control. The aim of this study was to
investigate the resting state functional MRI thalamic
hyper connectivity of stroke patients affected
by sleep apnoea (SA) with respect to cases not
affected, and to healthy controls (HC).
PATIENTS AND METHODS: A series of stabilized
strokes were submitted to 3T resting state
functional MRI imaging and full polysomnography.
The ventral-posterior-lateral thalamic nucleus was
used as seed.
RESULTS: At the between groups comparison
analysis, in SA cases versus HC, the regions significantly
hyper-connected with the seed were
those encoding noxious threats (frontal eye
field, somatosensory association, secondary visual
cortices). Comparisons between SA cases
versus those without SA, revealed in the former
group significantly increased connectivity with
regions modulating the response to stimuli independently
to their potentiality of threat (prefrontal,
primary and somatosensory association, superolateral
and medial-inferior temporal, associative
and secondary occipital ones). Further
significantly functionally hyper connections were
documented with regions involved also in the modulation
of breathing during sleep (pons, midbrain,
cerebellum, posterior cingulate cortices), and in
the modulation of breathing response to chemical
variations (anterior, posterior and para-hippocampal
cingulate cortices).
CONCLUSIONS: Our preliminary data support
the presence of functional hyper connectivity in
thalamic circuits modulating sensorial stimuli, in
patients with post-stroke sleep apnoea, possibly
influencing both their arousal ability and breathing
modulation during sleep
Developmental hypomyelination in Wolfram syndrome: New insights from neuroimaging and gene expression analyses
Wolfram syndrome is a rare multisystem disorder caused by mutations in WFS1 or CISD2 genes leading to brain structural abnormalities and neurological symptoms. These abnormalities appear in early stages of the disease. The pathogenesis of Wolfram syndrome involves abnormalities in the endoplasmic reticulum (ER) and mitochondrial dynamics, which are common features in several other neurodegenerative disorders. Mutations in WFS1 are responsible for the majority of Wolfram syndrome cases. WFS1 encodes for an endoplasmic reticulum (ER) protein, wolframin. It is proposed that wolframin deficiency triggers the unfolded protein response (UPR) pathway resulting in an increased ER stress-mediated neuronal loss. Recent neuroimaging studies showed marked alteration in early brain development, primarily characterized by abnormal white matter myelination. Interestingly, ER stress and the UPR pathway are implicated in the pathogenesis of some inherited myelin disorders like Pelizaeus-Merzbacher disease, and Vanishing White Matter disease. In addition, exploratory gene-expression network-based analyses suggest that WFS1 expression occurs preferentially in oligodendrocytes during early brain development. Therefore, we propose that Wolfram syndrome could belong to a category of neurodevelopmental disorders characterized by ER stress-mediated myelination impairment. Further studies of myelination and oligodendrocyte function in Wolfram syndrome could provide new insights into the underlying mechanisms of the Wolfram syndrome-associated brain changes and identify potential connections between neurodevelopmental disorders and neurodegeneration
Adaptive Deep Brain Stimulation: From Experimental Evidence Toward Practical Implementation.
Closed-loop adaptive deep brain stimulation (aDBS) can deliver individualized therapy at an unprecedented temporal precision for neurological disorders. This has the potential to lead to a breakthrough in neurotechnology, but the translation to clinical practice remains a significant challenge. Via bidirectional implantable brain-computer-interfaces that have become commercially available, aDBS can now sense and selectively modulate pathophysiological brain circuit activity. Pilot studies investigating different aDBS control strategies showed promising results, but the short experimental study designs have not yet supported individualized analyses of patient-specific factors in biomarker and therapeutic response dynamics. Notwithstanding the clear theoretical advantages of a patient-tailored approach, these new stimulation possibilities open a vast and mostly unexplored parameter space, leading to practical hurdles in the implementation and development of clinical trials. Therefore, a thorough understanding of the neurophysiological and neurotechnological aspects related to aDBS is crucial to develop evidence-based treatment regimens for clinical practice. Therapeutic success of aDBS will depend on the integrated development of strategies for feedback signal identification, artifact mitigation, signal processing, and control policy adjustment, for precise stimulation delivery tailored to individual patients. The present review introduces the reader to the neurophysiological foundation of aDBS for Parkinson's disease (PD) and other network disorders, explains currently available aDBS control policies, and highlights practical pitfalls and difficulties to be addressed in the upcoming years. Finally, it highlights the importance of interdisciplinary clinical neurotechnological research within and across DBS centers, toward an individualized patient-centered approach to invasive brain stimulation. Ā© 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society
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