To investigate grey (GM) and white matter (WM) abnormalities and their effects on cognitive and behavioral deficits in a large, phenotypically and genotypically well-characterized cohort of classic ...adult (aDM1, age at onset ≥ 20 years) or juvenile (jDM1, age at onset <20 years) patients with myotonic dystrophy type 1 (DM1).
A case-control study including 51 DM1 patients (17 jDM1 and 34 aDM1) and 34 controls was conducted at an academic medical center. Clinical, cognitive and structural MRI evaluations were obtained. Quantitative assessments of regional GM volumes, WM hyperintensities (WMHs), and microstructural WM tract damage were performed. The association between structural brain damage and clinical and cognitive findings was assessed.
DM1 patients showed a high prevalence of WMHs, severe regional GM atrophy including the key nodes of the sensorimotor and main cognitive brain networks, and WM microstructural damage of the interhemispheric, corticospinal, limbic and associative pathways. WM tract damage extends well beyond the focal WMHs. While aDM1 patients had severe patterns of GM atrophy and WM tract damage, in jDM1 patients WM abnormalities exceeded GM involvement. In DM1, WMHs and microstructural damage, but not GM atrophy, correlated with cognitive deficits.
WM damage, through a disconnection between GM structures, is likely to be the major contributor to cognitive impairment in DM1. Our MRI findings in aDM1 and jDM1 patients support the hypothesis of a degenerative (premature aging) origin of the GM abnormalities and of developmental changes as the principal substrates of microstructural WM alterations in DM1.
Longitudinal connectivity studies might guide our understanding of the underlying neurodegenerative processes. We report the results of a longitudinal study in patients at different stages of ...Parkinson's disease (PD), who performed motor and non-motor evaluations and serial resting state (RS) functional MRI (fMRI). Cluster analysis was applied to demographic and clinical data of 146 PD patients to define disease subtypes. Brain network functional alterations were assessed at baseline in PD relative to 60 healthy controls and every year for a maximum of 4 years in PD groups. Progression of brain network changes were compared between patient clusters using RS fMRI. The contribution of network changes in predicting clinical deterioration was explored. Two main PD clusters were identified: mild PD (86 patients) and moderate-to-severe PD (60 patients), with the latter group being older and having earlier onset, longer PD duration, more severe motor, non-motor and cognitive deficits. Within the mild patient cluster, two clinical subtypes were further identified: mild motor-predominant (43) and mild-diffuse (43), with the latter being older and having more frequent non-motor symptoms. Longitudinal functional connectivity changes vary across patients in different disease stages with the coexistence of hypo- and hyper-connectivity in all subtypes. RS fMRI changes were associated with motor, cognitive and non-motor evolution in PD patients. Baseline RS fMRI presaged clinical and cognitive evolution. Our network perspective was able to define trajectories of functional architecture changes according to PD stages and prognosis. RS fMRI may be an early biomarker of PD motor and non-motor progression.
This study assessed brain structural alterations in two diverse clinical forms of functional (psychogenic) dystonia (FD) - the typical fixed dystonia (FixFD) phenotype and the "mobile" dystonia ...(MobFD) phenotype, which has been recently described in one study. Forty-four FD patients (13 FixFD and 31 MobFD) and 43 healthy controls were recruited. All subjects underwent 3D T1-weighted and diffusion tensor (DT) magnetic resonance imaging (MRI). Cortical thickness, volumes of gray matter (GM) structures, and white matter (WM) tract integrity were assessed. Normal cortical thickness in both FD patient groups compared with age-matched healthy controls were found. When compared with FixFD, MobFD patients showed cortical thinning of the left orbitofrontal cortex, and medial and lateral parietal and cingulate regions bilaterally. Additionally, compared with controls, MobFD patients showed reduced volumes of the left nucleus accumbens, putamen, thalamus, and bilateral caudate nuclei, whereas MobFD patients compared with FixFD demonstrated atrophy of the right hippocampus and globus pallidus. Compared with both controls and MobFD cases, FixFD patients showed a severe disruption of WM architecture along the corpus callous, corticospinal tract, anterior thalamic radiations, and major long-range tracts bilaterally. This study showed different MRI patterns in two variants of FD. MobFD had alterations in GM structures crucial for sensorimotor processing, emotional, and cognitive control. On the other hand, FixFD patients were characterized by a global WM disconnection affecting main sensorimotor and emotional control circuits. These findings may have important implications in understanding the neural substrates underlying different phenotypic FD expression levels.
•Advanced algorithms can contribute to more efficient PD diagnosis and assessment.•Artificial Neural Networks gave the highest accuracy (>95%) for early PD detection.•SVM proved to be the most ...successful algorithm for symptom severity prediction.•The studies are diverse in terms of participants, methodology and outcome measure.•Validation of algorithms would benefit from increased collaboration of researchers.
Artificial intelligence, specifically machine learning, has found numerous applications in computer-aided diagnostics, monitoring and management of neurodegenerative movement disorders of parkinsonian type. These tasks are not trivial due to high inter-subject variability and similarity of clinical presentations of different neurodegenerative disorders in the early stages. This paper aims to give a comprehensive, high-level overview of applications of artificial intelligence through machine learning algorithms in kinematic analysis of movement disorders, specifically Parkinson’s disease (PD). We surveyed papers published between January 2007 and January 2019, within online databases, including PubMed and Science Direct, with a focus on the most recently published studies. The search encompassed papers dealing with the implementation of machine learning algorithms for diagnosis and assessment of PD using data describing motion of upper and lower extremities. This systematic review presents an overview of 48 relevant studies published in the abovementioned period, which investigate the use of artificial intelligence for diagnostics, therapy assessment and progress prediction in PD based on body kinematics. Different machine learning algorithms showed promising results, particularly for early PD diagnostics. The investigated publications demonstrated the potentials of collecting data from affordable and globally available devices. However, to fully exploit artificial intelligence technologies in the future, more widespread collaboration is advised among medical institutions, clinicians and researchers, to facilitate aligning of data collection protocols, sharing and merging of data sets.
Abstract Cortico-striatal-thalamic network functional connectivity (FC) and its relationship with levodopa (L-dopa) were investigated in 69 patients with hemiparkinsonism (25 drug-naïve n-PD and 44 ...under stable/optimized dopaminergic treatment t-PD) and 27 controls. Relative to controls, n-PD patients showed an increased FC between the left and the right basal ganglia, and a decreased connectivity of the affected caudate nucleus and thalamus with the ipsilateral frontal and insular cortices. Compared with both controls and n-PD patients, t-PD patients showed a decreased FC among the striatal and thalamic regions, and an increased FC between the striatum and temporal cortex, and between the thalamus and several sensorimotor, parietal, temporal, and occipital regions. In both n-PD and t-PD, patients with more severe motor disability had an increased striatal and/or thalamic FC with temporal, parietal, occipital, and cerebellar regions. Cortico-striatal-thalamic functional abnormalities occur in patients with hemiparkinsonism, antecede the onset of the motor symptoms on the opposite body side and are modulated by L-dopa. In patients with hemiparkinsonism, L-dopa is likely to facilitate a compensation of functional abnormalities possibly through an increased thalamic FC.
Abstract Depression and apathy are among the most common neuropsychiatric disturbances in Parkinson's disease (PD), and among the most important factors associated with a poor quality of life. ...However, their neural bases remain unclear. The results of the magnetic resonance imaging (MRI) studies on depression in PD differ dramatically. Some of them proposed a role of morphologic changes in the mediodorsal thalamus. In contrast to previous voxel-based morphometry (VBM) data, our study did not confirm a decrease in gray matter (GM) density in any brain region of depressed PD patients. Instead, a more severe white matter (WM) loss in the right frontal lobe was found, including the anterior cingulate bundle and the inferior orbitofrontal (OF) region. We suggested that the negative correlation between the severity of depression and WM density in the right OF region reinforces the hypothesis of depression in PD as a “disconnection syndrome”. Only one MRI study using VBM found that high apathy scores correlated with low GM density values in the right (posterior) cingulate gyrus and the bilateral inferior frontal gyrus, in line with the findings in Alzheimer's disease and elderly adults with major depression.
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