Harmonizing multisite data with the ComBat method for enhanced Parkinson's disease diagnosis via DAT-SPECT.

Background: Dopamine transporter single-photon emission computed tomography (DAT-SPECT) is a crucial tool for evaluating patients with Parkinson's disease (PD). However, its implication is limited by inter-site variability in large multisite clinical trials. To overcome the limitation, a conventional prospective correction method employs linear regression with phantom scanning, which is effective yet available only in a prospective manner. An alternative, although relatively underexplored, involves retrospective modeling using a statistical method known as "combatting batch effects when combining batches of gene expression microarray data" (ComBat). Methods: We analyzed DAT-SPECT-specific binding ratios (SBRs) derived from 72 healthy older adults and 81 patients with PD registered in four clinical sites. We applied both the prospective correction and the retrospective ComBat correction to the original SBRs. Next, we compared the performance of the original and two corrected SBRs to differentiate the PD patients from the healthy controls. Diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC). Results: The original SBRs were 6.13 ± 1.54 (mean ± standard deviation) and 2.03 ± 1.41 in the control and PD groups, respectively. After the prospective correction, the mean SBRs were 6.52 ± 1.06 and 2.40 ± 0.99 in the control and PD groups, respectively. After the retrospective ComBat correction, the SBRs were 5.25 ± 0.89 and 2.01 ± 0.73 in the control and PD groups, respectively, resulting in substantial changes in mean values with fewer variances. The original SBRs demonstrated fair performance in differentiating PD from controls (Hedges's g = 2.76; AUC-ROC = 0.936). Both correction methods improved discrimination performance. The ComBat-corrected SBR demonstrated comparable performance (g = 3.99 and AUC-ROC = 0.987) to the prospectively corrected SBR (g = 4.32 and AUC-ROC = 0.992) for discrimination. Conclusion: Although we confirmed that SBRs fairly discriminated PD from healthy older adults without any correction, the correction methods improved their discrimination performance in a multisite setting. Our results support the utility of harmonization methods with ComBat for consolidating SBR-based diagnosis or stratification of PD in multisite studies. Nonetheless, given the substantial changes in the mean values of ComBat-corrected SBRs, caution is advised when interpreting them.

Functional connectivity-based classification of rapid eye movement sleep behavior disorder.

BACKGROUND: Isolated rapid eye movement sleep behavior disorder (iRBD) is a clinically important parasomnia syndrome preceding α-synucleinopathies, thereby prompting us to develop methods for evaluating latent brain states in iRBD. Resting-state functional magnetic resonance imaging combined with a machine learning-based classification technology may help us achieve this purpose. METHODS: We developed a machine learning-based classifier using functional connectivity to classify 55 patients with iRBD and 97 healthy elderly controls (HC). Selecting 55 HCs randomly from the HC dataset 100 times, we conducted a classification of iRBD and HC for each sampling, using functional connectivity. Random forest ranked the importance of functional connectivity, which was subsequently used for classification with logistic regression and a support vector machine. We also conducted correlation analysis of the selected functional connectivity with subclinical variations in motor and non-motor functions in the iRBDs. RESULTS: Mean classification performance using logistic regression was 0.649 for accuracy, 0.659 for precision, 0.662 for recall, 0.645 for f1 score, and 0.707 for the area under the receiver operating characteristic curve (p < 0.001 for all). The result was similar in the support vector machine. The classifier used functional connectivity information from nine connectivities across the motor and somatosensory areas, parietal cortex, temporal cortex, thalamus, and cerebellum. Inter-individual variations in functional connectivity were correlated with the subclinical motor and non-motor symptoms of iRBD patients. CONCLUSIONS: Machine learning-based classifiers using functional connectivity may be useful to evaluate latent brain states in iRBD.

Contribution of amyloid and putative Lewy body pathologies in neuropsychiatric symptoms.

OBJECTIVES: Neuropsychiatric symptom could be useful for detecting patients with prodromal dementia. Similarities and differences in the NPSs between preclinical/prodromal Alzheimer's disease (AD) and prodromal Parkinson's disease dementia (PDD)/Dementia with Lewy bodies (DLB) may exist. This study aimed to compare the NPSs between preclinical/prodromal AD and prodromal PDD/DLB. METHODS: One hundred and three participants without dementia aged ≥50 years were included in this study. The mild behavioral impairment (MBI) total score and the MBI scores for each domain were calculated using the neuropsychiatric inventory questionnaire score. Participants were divided into five groups based on the clinical diagnosis by neurologists or psychiatrists in each institution based on the results of the amyloid positron emission tomography and dopamine transporter single photon emission computed tomography (DAT-SPECT): Group 1: amyloid-positive and abnormal DAT-SPECT, Group 2: amyloid-negative and abnormal DAT-SPECT, Group 3: amyloid-positive and normal DAT-SPECT, Group 4: mild cognitive impairment unlikely due to AD with normal DAT-SPECT, and Group 5: cognitively normal with amyloid-negative and normal DAT-SPECT. RESULTS: The MBI abnormal perception or thought content scores were significantly higher in Group 1 than Group 5 (Bonferroni-corrected p = 0.012). The MBI total score (Bonferroni-corrected p = 0.011) and MBI impulse dyscontrol score (Bonferroni-corrected p = 0.033) in Group 4 were significantly higher than those in Group 5. CONCLUSION: The presence of both amyloid and putative Lewy body pathologies may be associated with psychotic symptoms.

Interactions across emotional, cognitive and subcortical motor networks underlying freezing of gait.

Freezing of gait (FOG) is a gait disorder affecting patients with Parkinson's disease (PD) and related disorders. The pathophysiology of FOG is unclear because of its phenomenological complexity involving motor, cognitive, and emotional aspects of behavior. Here we used resting-state functional MRI to retrieve functional connectivity (FC) correlated with the New FOG questionnaire (NFOGQ) reflecting severity of FOG in 67 patients with PD. NFOGQ scores were correlated with FCs in the extended basal ganglia network (BGN) involving the striatum and amygdala, and in the extra-cerebellum network (CBLN) involving the frontoparietal network (FPN). These FCs represented interactions across the emotional (amygdala), subcortical motor (BGN and CBLN), and cognitive networks (FPN). Using these FCs as features, we constructed statistical models that explained 40% of the inter-individual variances of FOG severity and that discriminated between PD patients with and without FOG. The amygdala, which connects to the subcortical motor (BGN and CBLN) and cognitive (FPN) networks, may have a pivotal role in interactions across the emotional, cognitive, and subcortical motor networks. Future refinement of the machine learning-based classifier using FCs may clarify the complex pathophysiology of FOG further and help diagnose and evaluate FOG in clinical settings.

It Is Time to Study Overlapping Molecular and Circuit Pathophysiologies in Alzheimer's and Lewy Body Disease Spectra.

Alzheimer's disease (AD) is the leading cause of dementia due to neurodegeneration and is characterized by extracellular senile plaques composed of amyloid ß1 - 42 (Aß) as well as intracellular neurofibrillary tangles consisting of phosphorylated tau (p-tau). Dementia with Lewy bodies constitutes a continuous spectrum with Parkinson's disease, collectively termed Lewy body disease (LBD). LBD is characterized by intracellular Lewy bodies containing α-synuclein (α-syn). The core clinical features of AD and LBD spectra are distinct, but the two spectra share common cognitive and behavioral symptoms. The accumulation of pathological proteins, which acquire pathogenicity through conformational changes, has long been investigated on a protein-by-protein basis. However, recent evidence suggests that interactions among these molecules may be critical to pathogenesis. For example, Aß/tau promotes α-syn pathology, and α-syn modulates p-tau pathology. Furthermore, clinical evidence suggests that these interactions may explain the overlapping pathology between AD and LBD in molecular imaging and post-mortem studies. Additionally, a recent hypothesis points to a common mechanism of prion-like progression of these pathological proteins, via neural circuits, in both AD and LBD. This suggests a need for understanding connectomics and their alterations in AD and LBD from both pathological and functional perspectives. In AD, reduced connectivity in the default mode network is considered a hallmark of the disease. In LBD, previous studies have emphasized abnormalities in the basal ganglia and sensorimotor networks; however, these account for movement disorders only. Knowledge about network abnormalities common to AD and LBD is scarce because few previous neuroimaging studies investigated AD and LBD as a comprehensive cohort. In this paper, we review research on the distribution and interactions of pathological proteins in the brain in AD and LBD, after briefly summarizing their clinical and neuropsychological manifestations. We also describe the brain functional and connectivity changes following abnormal protein accumulation in AD and LBD. Finally, we argue for the necessity of neuroimaging studies that examine AD and LBD cases as a continuous spectrum especially from the proteinopathy and neurocircuitopathy viewpoints. The findings from such a unified AD and Parkinson's disease (PD) cohort study should provide a new comprehensive perspective and key data for guiding disease modification therapies targeting the pathological proteins in AD and LBD.

Prefrontal network dysfunctions in rapid eye movement sleep behavior disorder.

INTRODUCTION: Resting-state functional connectivity magnetic resonance imaging (rsfcMRI) of rapid eye movement (REM) sleep behavior disorder (RBD) may provide an early biomarker of α-synucleinopathy. However, few rsfcMRI studies have examined cognitive networks. To elucidate brain network changes in RBD, we performed rsfcMRI in patients with polysomnography-confirmed RBD and healthy controls (HCs), with a sufficiently large sample size in each group. METHODS: We analyzed rsfcMRI data from 50 RBD patients and 70 age-matched HCs. Although RBD patients showed no motor signs, some exhibited autonomic and cognitive problems. Several resting-state functional networks were extracted by group independent component analysis from HCs, including the executive-control (ECN), default-mode (DMN), basal ganglia (BGN), and sensory-motor (SMN) networks. Functional connectivity (FC) was compared between groups using dual regression analysis. In the RBD group, correlation analysis was performed between FC and clinical/cognitive scales. RESULTS: Patients with RBD showed reduced striatal-prefrontal FC in ECN, consistent with executive dysfunctions. No abnormalities were found in DMN. In the motor networks, we identified reduced midbrain-pallidum FC in BGN and reduced motor and somatosensory cortex FC in SMN. CONCLUSION: We found abnormal ECN and normal DMN as a possible hallmark of cognitive dysfunctions in early α-synucleinopathies. We replicated abnormalities in BGN and SMN corresponding to subclinical movement disorder of RBD. RsfcMRI may provide an early biomarker of both cognitive and motor network dysfunctions of α-synucleinopathies.

Discriminating chorea-acanthocytosis from Huntington's disease with single-case voxel-based morphometry analysis.

BACKGROUND AND PURPOSE: Chorea-acanthocytosis is clinically difficult to distinguish from Huntington's disease because these disorders have similar symptoms and MR imaging findings. We evaluated the usefulness of single-case voxel-based morphometry (VBM) analysis for differentiating the two diseases as well as VBM analysis. MATERIALS AND METHODS: We examined five genetically proven chorea-acanthocytosis patients and 11 Huntington's disease patients to detect differences in the gray and white matter atrophic pattern by using single-case VBM analysis in each patient and their clinical findings. We also evaluated VBM analysis for a group comparison in both disease and control groups. RESULTS: The single-case VBM analysis results demonstrated a gray matter volume loss in caudate nucleus in all 16 patients. A characteristic symmetrical white matter volume loss was detected in globus pallidus, putamen, and thalamus on both sides in all the chorea-acanthocytosis patients, but this pattern of atrophy was not seen in any of the Huntington's disease patients. With the VBM analysis, a significant gray matter volume loss was noted in caudate nucleus on both sides in chorea-acanthocytosis patients compared with Huntington's disease patients, and a more extensive white matter volume loss around the basal ganglia and thalamus was observed in chorea-acanthocytosis patients compared to Huntington's disease patients, consistent with the single-case VBM analysis results. Genetic testing identified two novel pathogenic mutations, exon 1 c.16_22delGTGGTCG and exon 55 c.7736-7739delGAGA in a chorea-acanthocytosis patient. CONCLUSIONS: Single-case VBM analysis may be useful to differentiate chorea-acanthocytosis from Huntington's disease with a focus on white matter atrophy.

Novel pathogenic VPS13A gene mutations in Japanese patients with chorea-acanthocytosis.

OBJECTIVE: To identify mutations in vacuolar protein sorting 13A (VPS13A) for Japanese patients with suspected chorea-acanthocytosis (ChAc). METHODS: We performed a comprehensive mutation screen, including sequencing and copy number variation (CNV) analysis of the VPS13A gene, and chorein Western blotting of erythrocyte ghosts. As the results of the analysis, 17 patients were molecularly diagnosed with ChAc. In addition, we investigated the distribution of VPS13A gene mutations and clinical symptoms in a total of 39 molecularly diagnosed Japanese patients with ChAc, including 22 previously reported cases. RESULTS: We identified 11 novel pathogenic mutations, including 1 novel CNV. Excluding 5 patients with the unknown symptoms, 97.1% of patients displayed various neuropsychiatric symptoms or forms of cognitive dysfunction during the course of disease. The patients carrying the 2 major mutations representing over half of the mutations, exon 60-61 deletion and exon 37 c.4411C>T (R1471X), were localized in western Japan. CONCLUSIONS: We identified 13 different mutations in VPS13A, including 11 novel mutations, and verified the clinical manifestations in 39 Japanese patients with ChAc.

Thalamic involvement determined using VSRAD advance on MRI and easy Z-score analysis of 99mTc-ECD-SPECT in Gerstmann-Sträussler-Scheinker syndrome with P102L mutation.

Gerstmann-Sträussler-Scheinker syndrome caused by the P102L mutation in the prion protein gene (GSS102) is usually characterized by the onset of slowly progressive cerebellar ataxia, with dementia occurring much later. Because of the relatively long disease course and the prominence of progressive cerebellar ataxia in the early stage, GSS102 is often misdiagnosed as other neurodegenerative disorders. We present two cases of genetically proven GSS102L, both of which present with atrophy and decreased blood flow of the thalamus as determined by voxel-based specific regional analysis system for Alzheimer's disease (VSRAD) advance software and easy Z-score analysis for 99mTc-ethyl cysteinate dimer-SPECT, respectively. These thalamic abnormalities have not been fully evaluated to date, and detecting them might be useful for differentiating GSS102 from other neurodegenerative disorders.