Intra- and Inter-Modular Connectivity Alterations in the Brain Structural Network of Spinocerebellar Ataxia Type 3

Brain Dynamics Complexity as a Signature of Cognitive Decline in Parkinson's Disease

BACKGROUND

Higuchi's fractal dimension (FD) captures brain dynamics complexity and may be a promising method to analyze resting-state functional magnetic resonance imaging (fMRI) data and detect the neuronal interaction complexity underlying Parkinson's disease (PD) cognitive decline.

OBJECTIVES

The aim was to compare FD with a more established index of spontaneous neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), and identify through machine learning (ML) models which method could best distinguish across PD-cognitive states, ranging from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD). Finally, the aim was to explore correlations between fALFF and FD with clinical and cognitive PD features.

METHODS

Among 118 PD patients age-, sex-, and education matched with 35 healthy controls, 52 were classified with PD-NC, 46 with PD-MCI, and 20 with PDD based on an extensive cognitive and clinical evaluation. fALFF and FD metrics were computed on rs-fMRI data and used to train ML models.

RESULTS

FD outperformed fALFF metrics in differentiating between PD-cognitive states, reaching an overall accuracy of 78% (vs. 62%). PD showed increased neuronal dynamics complexity within the sensorimotor network, central executive network (CEN), and default mode network (DMN), paralleled by a reduction in spontaneous neuronal activity within the CEN and DMN, whose increased complexity was strongly linked to the presence of dementia. Further, we found that some DMN critical hubs correlated with worse cognitive performance and disease severity.

CONCLUSIONS

Our study indicates that PD-cognitive decline is characterized by an altered spontaneous neuronal activity and increased temporal complexity, involving the CEN and DMN, possibly reflecting an increased segregation of these networks. Therefore, we propose FD as a prognostic biomarker of PD-cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Fractal Dimension Studies of the Brain Shape in Aging and Neurodegenerative Diseases

The fractal dimension is a morphometric measure that has been used to investigate the changes of brain shape complexity in aging and neurodegenerative diseases. This chapter reviews fractal dimension studies in aging and neurodegenerative disorders in the literature. Research has shown that the fractal dimension of the left cerebral hemisphere increases until adolescence and then decreases with aging, while the fractal dimension of the right hemisphere continues to increase until adulthood. Studies in neurodegenerative diseases demonstrated a decline in the fractal dimension of the gray matter and white matter in Alzheimer's disease, amyotrophic lateral sclerosis, and spinocerebellar ataxia. In multiple sclerosis, the white matter fractal dimension decreases, but conversely, the fractal dimension of the gray matter increases at specific stages of disease. There is also a decline in the gray matter fractal dimension in frontotemporal dementia and multiple system atrophy of the cerebellar type and in the white matter fractal dimension in epilepsy and stroke. Region-specific changes in fractal dimension have also been found in Huntington's disease and Parkinson's disease. Associations were found between the fractal dimension and clinical scores, showing the potential of the fractal dimension as a marker to monitor brain shape changes in normal or pathological processes and predict cognitive or motor function.

Altered large-scale individual-based morphological brain network in spinocerebellar ataxia type 3

BACKGROUND

Accumulating evidences indicate regional gray matter (GM) morphology atrophy in spinocerebellar ataxia type 3 (SCA3); however, whether large-scale morphological brain networks (MBNs) undergo widespread reorganization in these patients remains unclear.

OBJECTIVE

To investigate the topological organization of large-scale individual-based MBNs in SCA3 patients.

METHODS

The individual-based MBNs were constructed based on the inter-regional morphological similarity of GM regions. Graph theoretical analysis was taken to assess GM structural connectivity in 76 symptomatic SCA3, 24 pre-symptomatic SCA3, and 54 healthy normal controls (NCs). Topological parameters of the resulting graphs and network-based statistics analysis were compared among symptomatic SCA3, pre-symptomatic SCA3, and NCs groups. The inner association between network properties and clinical variables was further analyzed.

RESULTS

Compared to NCs and pre-symptomatic SCA3 patients, symptomatic SCA3 indicated significantly decreased integration and segregation, a shift to "weaker small-worldness", characterized by decreased Cp , lower Eloc, and Eglob (all p < 0.005). Regarding nodal properties, symptomatic SCA3 exhibited significantly decreased nodal profiles in the central executive network (CEN)-related left inferior frontal gyrus, limbic regions involving the bilateral amygdala, left hippocampus, and bilateral pallidum, thalamus; and increased nodal degree, efficiency in bilateral caudate (all pFDR  <0.05). Meanwhile, clinical variables were correlated with altered nodal profiles (pFDR  ≤0.029). SCA3-related subnetwork was closely interrelated with dorsolateral cortico-striatal circuitry extending to orbitofrontal-striatal circuits and dorsal visual systems (lingual gyrus-striatal).

CONCLUSION

Symptomatic SCA3 patients undergo an extensive and significant reorganization in large-scale individual-based MBNs, probably due to disrupted prefrontal cortico-striato-thalamo-cortical loops, limbic-striatum circuitry, and enhanced connectivity in the neostriatum. This study highlights the crucial role of abnormal morphological connectivity alterations beyond the pattern of brain atrophy, which might pave the way for therapeutic development in the future.

Decreased Brain Structural Network Connectivity in Patients with Mild Cognitive Impairment: A Novel Fractal Dimension Analysis

Mild cognitive impairment (MCI) is widely regarded to be the intermediate stage to Alzheimer's disease. Cerebral morphological alteration in cortical subregions can provide an accurate predictor for early recognition of MCI. Thirty patients with MCI and thirty healthy control subjects participated in this study. The Desikan-Killiany cortical atlas was applied to segment participants' cerebral cortex into 68 subregions. A complexity measure termed fractal dimension (FD) was applied to assess morphological changes in cortical subregions of participants. The MCI group revealed significantly decreased FD values in the bilateral temporal lobes, right parietal lobe including the medial temporal, fusiform, para hippocampal, and also the orbitofrontal lobes. We further proposed a novel FD-based brain structural network to compare network parameters, including intra- and inter-lobular connectivity between groups. The control group had five modules, and the MCI group had six modules in their brain networks. The MCI group demonstrated shrinkage of modular sizes with fewer components integrated, and significantly decreased global modularity in the brain network. The MCI group had lower intra- and inter-lobular connectivity in all lobes. Between cerebral lobes, the MCI patients may maintain nodal connections between both hemispheres to reduce connectivity loss in the lateral hemispheres. The method and results presented in this study could be a suitable tool for early detection of MCI.

Magnetic Resonance Imaging and Its Clinical Correlation in Spinocerebellar Ataxia Type 3: A Systematic Review

Background

Spinocerebellar ataxia type 3 (SCA3) is a complex cerebrocerebellar disease primarily characterized by ataxia symptoms alongside motor and cognitive impairments. The heterogeneous clinical presentation of SCA3 necessitates correlations between magnetic resonance imaging (MRI) and clinical findings in reflecting progressive disease changes. At present, an attempt to systematically examine the brain-behavior relationship in SCA3, specifically, the correlation between MRI and clinical findings, is lacking.

Objective

We investigated the association strength between MRI abnormality and each clinical symptom to understand the brain-behavior relationship in SCA3.

Methods

We conducted a systematic review on Medline and Scopus to review studies evaluating the brain MRI profile of SCA3 using structural MRI (volumetric, voxel-based morphometry, surface analysis), magnetic resonance spectroscopy, and diffusion tensor imaging, including their correlations with clinical outcomes.

Results

Of 1,767 articles identified, 29 articles met the eligibility criteria. According to the National Institutes of Health quality assessment tool for case-control studies, all articles were of excellent quality. This systematic review found that SCA3 neuropathology contributes to widespread brain degeneration, affecting the cerebellum and brainstem. The disease gradually impedes the cerebral cortex and basal ganglia in the late stages of SCA3. Most findings reported moderate correlations (r = 0.30–0.49) between MRI features in several regions and clinical findings. Regardless of the MRI techniques, most studies focused on the brainstem and cerebellum.

Conclusions

Clinical findings suggest that rather than individual brain regions, the connectivity between different brain regions in distributed networks (i.e., cerebellar-cerebral network) may be responsible for motor and neurocognitive function in SCA3. This review highlights the importance of evaluating the progressive changes of the cerebellar-cerebral networks in SCA3 patients, specifically the functional connectivity. Given the relative lack of knowledge about functional connectivity on SCA3, future studies should investigate possible functional connectivity abnormalities in SCA3 using fMRI.

Fractal dimension of the brain in neurodegenerative disease and dementia: A systematic review

Sensitive and specific antemortem biomarkers of neurodegenerative disease and dementia are crucial to the pursuit of effective treatments, required both to reliably identify disease and to track its progression. Atrophy is the structural magnetic resonance imaging (MRI) hallmark of neurodegeneration. However in most cases it likely indicates a relatively advanced stage of disease less susceptible to treatment as some disease processes begin decades prior to clinical onset. Among emerging metrics that characterise brain shape rather than volume, fractal dimension (FD) quantifies shape complexity. FD has been applied in diverse fields of science to measure subtle changes in elaborate structures. We review its application thus far to structural MRI of the brain in neurodegenerative disease and dementia. We identified studies involving subjects who met criteria for mild cognitive impairment, Alzheimer's Disease, Vascular Dementia, Lewy Body Dementia, Frontotemporal Dementia, Amyotrophic Lateral Sclerosis, Parkinson's Disease, Huntington's Disease, Multiple Systems Atrophy, Spinocerebellar Ataxia and Multiple Sclerosis. The early literature suggests that neurodegenerative disease processes are usually associated with a decline in FD of the brain. The literature includes examples of disease-related change in FD occurring independently of atrophy, which if substantiated would represent a valuable advantage over other structural imaging metrics. However, it is likely to be non-specific and to exhibit complex spatial and temporal patterns. A more harmonious methodological approach across a larger number of studies as well as careful attention to technical factors associated with image processing and FD measurement will help to better elucidate the metric's utility.

Alteration of the Intra- and Inter-Lobe Connectivity of the Brain Structural Network in Normal Aging

The morphological changes in cortical parcellated regions during aging and whether these atrophies may cause brain structural network intra- and inter-lobe connectivity alterations are subjects that have been minimally explored. In this study, a novel fractal dimension-based structural network was proposed to measure atrophy of 68 parcellated cortical regions. Alterations of structural network parameters, including intra- and inter-lobe connectivity, were detected in a middle-aged group (30–45 years old) and an elderly group (50–65 years old). The elderly group exhibited significant lateralized atrophy in the left hemisphere, and most of these fractal dimension atrophied regions were included in the regions of the “last-in, first-out” model. Globally, the elderly group had lower modularity values, smaller component size modules, and fewer bilateral association fibers. They had lower intra-lobe connectivity in the frontal and parietal lobes, but higher intra-lobe connectivity in the temporal and occipital lobes. Both groups exhibited similar inter-lobe connecting pattern. The elderly group revealed separations, sparser long association fibers, commissural fibers, and lateral inter-lobe connectivity lost effect, mainly in the right hemisphere. New wiring and reconfiguring modules may have occurred within the brain structural network to compensate for connectivity, decreasing and preventing functional loss in cerebral intra- and inter-lobe connectivity.