Functional Connectomics and Disease Progression in Drug-Naïve Parkinson's Disease Patients

Altered domain-specific striatal functional connectivity in patients with Parkinson's disease and urinary symptoms

BACKGROUND

In this study, we aimed at investigating the possible association of urinary symptoms with whole-brain MRI resting-state functional connectivity (FC) alterations from distinct striatal subregions in a large cohort of early PD patients.

METHODS

Seventy-nine drug-naive PD patients (45 PD-urinary+/34 PD-urinary-) and 38 healthy controls (HCs) were consecutively enrolled. Presence/absence of urinary symptoms were assessed by means of the Nonmotor Symptom Scale - domain 7. Using an a priori connectivity-based domain-specific parcellation, we defined three ROIs (per each hemisphere) for different striatal functional subregions (sensorimotor, limbic and cognitive) from which seed-based FC voxel-wise analyses were conducted over the whole brain.

RESULTS

Compared to PD-urinary-, PD-urinary+ patients showed increased FC between striatal regions and motor and premotor/supplementary motor areas as well as insula/anterior dorsolateral PFC. Compared to HC, PD-urinary+ patients presented decreased FC between striatal regions and parietal, insular and cingulate cortices.

CONCLUSIONS

Our findings revealed a specific pattern of striatal FC alteration in PD patients with urinary symptoms, potentially associated to altered stimuli perception and sensorimotor integration even in the early stages. These results may potentially help clinicians to design more effective and tailored rehabilitation and neuromodulation protocols for PD patients.

Insular and limbic abnormal functional connectivity in early-stage Parkinson's disease patients with autonomic dysfunction

Autonomic symptoms in Parkinson's disease result from variable involvement of the central and peripheral systems, but many aspects remain unclear. The analysis of functional connectivity has shown promising results in assessing the pathophysiology of Parkinson's disease. This study aims to investigate the association between autonomic symptoms and cortical functional connectivity in early Parkinson's disease patients using high-density EEG. 53 early Parkinson's disease patients (F/M 18/35) and 49 controls (F/M 20/29) were included. Autonomic symptoms were evaluated using the Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score. Data were recorded with a 64-channel EEG system. We analyzed cortical functional connectivity, based on weighted phase-lag index, in θ-α-β-low-γ bands. A network-based statistic was used to perform linear regression between Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score and functional connectivity in Parkinson's disease patients. We observed a positive relation between the Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score and α-functional connectivity (network τ = 2.8, P = 0.038). Regions with higher degrees were insula and limbic lobe. Moreover, we found positive correlations between the mean connectivity of this network and the gastrointestinal, cardiovascular, and thermoregulatory domains of Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction. Our results revealed abnormal functional connectivity in specific areas in Parkinson's disease patients with greater autonomic symptoms. Insula and limbic areas play a significant role in the regulation of the autonomic system. Increased functional connectivity in these regions might represent the central compensatory mechanism of peripheral autonomic dysfunction in Parkinson's disease.

Early effect of onabotulinumtoxinA on EEG-based functional connectivity in patients with chronic migraine: A pilot study

OBJECTIVE

In this pilot prospective cohort study, we aimed to evaluate, using high-density electroencephalography (HD-EEG), the longitudinal changes in functional connectivity (FC) in patients with chronic migraine (CM) treated with onabotulinumtoxinA (OBTA).

BACKGROUND

OBTA is a treatment for CM. Several studies have shown the modulatory action of OBTA on the central nervous system; however, research on migraine is limited.

METHODS

This study was conducted at the Neurology Unit of "Policlinico Tor Vergata," Rome, Italy, and included 12 adult patients with CM treated with OBTA and 15 healthy controls (HC). Patients underwent clinical scales at enrollment (T0) and 3 months (T1) from the start of treatment. HD-EEG was recorded using a 64-channel system in patients with CM at T0 and T1. A source reconstruction method was used to identify brain activity. FC in δ-θ-α-β-low-γ bands was analyzed using the weighted phase-lag index. FC changes between HCs and CM at T0 and T1 were assessed using cross-validation methods to estimate the results' reliability.

RESULTS

Compared to HCs at T0, patients with CM showed hyperconnected networks in δ (p = 0.046, area under the receiver operating characteristic curve [AUC: 0.76-0.98], Cohen's κ [0.65-0.93]) and β (p = 0.031, AUC [0.68-0.95], Cohen's κ [0.51-0.84]), mainly involving orbitofrontal, occipital, temporal pole and orbitofrontal, superior temporal, occipital, cingulate areas, and hypoconnected networks in α band (p = 0.029, AUC [0.80-0.99], Cohen's κ [0.42-0.77]), predominantly involving cingulate, temporal pole, and precuneus. Patients with CM at T1, compared to T0, showed hypoconnected networks in δ band (p = 0.032, AUC [0.73-0.99], Cohen's κ [0.53-0.90]) and hyperconnected networks in α band (p = 0.048, AUC [0.58-0.93], Cohen's κ [0.37-0.78]), involving the sensorimotor, orbitofrontal, cingulate, and temporal cortex.

CONCLUSION

These preliminary results showed that patients with CM presented disrupted EEG-FC compared to controls restored by a single session of OBTA treatment, suggesting a primary central modulatory action of OBTA.

Brain network centrality and connectivity are associated with clinical subtypes and disease progression in Parkinson's disease

To investigate brain network centrality and connectivity alterations in different Parkinson's disease (PD) clinical subtypes using resting-state functional magnetic resonance imaging (RS-fMRI), and to explore the correlation between baseline connectivity changes and the clinical progression. Ninety-two PD patients were enrolled at baseline, alongside 38 age- and sex-matched healthy controls. Of these, 85 PD patients underwent longitudinal assessments with a mean of 2.75 ± 0.59 years. Two-step cluster analysis integrating comprehensive motor and non-motor manifestations was performed to define PD subtypes. Degree centrality (DC) and secondary seed-based functional connectivity (FC) were applied to identify brain network centrality and connectivity changes among groups. Regression analysis was used to explore the correlation between baseline connectivity changes and clinical progression. Cluster analysis identified two main PD subtypes: mild PD and moderate PD. Two different subtypes within the mild PD were further identified: mild motor-predominant PD and mild-diffuse PD. Accordingly, the disrupted DC and seed-based FC in the left inferior frontal orbital gyrus and left superior occipital gyrus were severe in moderate PD. The DC and seed-based FC alterations in the right gyrus rectus and right postcentral gyrus were more severe in mild-diffuse PD than in mild motor-predominant PD. Moreover, disrupted DC were associated with clinical manifestations at baseline in patients with PD and predicted motor aspects progression over time. Our study suggested that brain network centrality and connectivity changes were different among PD subtypes. RS-fMRI holds promise to provide an objective assessment of subtype-related connectivity changes and predict disease progression in PD.

Brain Connectivity Networks Constructed Using MRI for Predicting Patterns of Atrophy Progression in Parkinson Disease

Background Whether connectome mapping of structural and functional connectivity across the brain could be used to predict patterns of atrophy progression in patients with mild Parkinson disease (PD) has not been well studied. Purpose To assess the structural and functional connectivity of brain regions in healthy controls and its relationship with the spread of gray matter (GM) atrophy in patients with mild PD. Materials and Methods This prospective study included participants with mild PD and controls recruited from a single center between January 2012 and December 2023. Participants with PD underwent three-dimensional T1-weighted brain MRI, and the extent of regional GM atrophy was determined at baseline and every year for 3 years. The structural and functional brain connectome was constructed using diffusion tensor imaging and resting-state functional MRI in healthy controls. Disease exposure (DE) indexes-indexes of the pathology of each brain region-were defined as a function of the structural or functional connectivity of all the connected regions in the healthy connectome and the severity of atrophy of the connected regions in participants with PD. Partial correlations were tested between structural and functional DE indexes of each GM region at 1- or 2-year follow-up and atrophy progression at 2- or 3-year follow-up. Prediction models of atrophy at 2- or 3-year follow-up were constructed using exhaustive feature selection. Results A total of 86 participants with mild PD (mean age at MRI, 60 years ± 8 [SD]; 48 male) and 60 healthy controls (mean age at MRI, 62 years ± 9; 31 female) were included. DE indexes at 1 and 2 years were correlated with atrophy at 2 and 3 years (r range, 0.22-0.33; P value range, .002-.04). Models including DE indexes predicted GM atrophy accumulation over 3 years in the right caudate nucleus and some frontal, parietal, and temporal brain regions (R2 range, 0.40-0.61; all P < .001). Conclusion The structural and functional organization of the brain connectome plays a role in atrophy progression in the early stages of PD. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yamada in this issue.

Altered functional-structural coupling may predict Parkinson's patient's depression

We aimed to elucidate the neurobiological basis of depression in Parkinson's disease and identify potential imaging markers for depression in patients with Parkinson's disease. We recruited 43 normal controls (NC), 46 depressed Parkinson's disease patients (DPD) and 56 non-depressed Parkinson's disease (NDPD). All participants underwent routine T2-weighted, T2Flair, and resting-state scans on the same 3.0 T magnetic resonance imaging (MRI) scanner at our hospital. Pre-processing includes calculating surface-based Regional Homogeneity (2DReHo) and cortical thickness. Then we defined the correlation coefficient between 2DReHo and cortical thickness as the functional-structural coupling index. Between-group comparisons were conducted on the Fisher's Z-transformed correlation coefficients. To identify specific regions of decoupling, the 2DReHo for each participant were divided by cortical thickness at each vertex, followed by threshold-free cluster enhancement (TFCE) multiple comparison correction. Binary logistic regression analysis was performed with DPD as the dependent variable, and significantly altered indicators as the independent variables. Receiver operating characteristic curves were constructed to compare the diagnostic performance of individual predictors and combinations using R and MedCalc software. DPD patients exhibited a significantly lower whole-brain functional-structural coupling index than NDPD patients and NC. Abnormal functional-structural coupling was primarily observed in the left inferior parietal lobule and right primary and early visual cortices in DPD patients. Receiver operating characteristic analysis revealed that the combination of cortical functional-structural coupling, surface-based ReHo, and thickness had the best diagnostic performance, achieving a sensitivity of 65% and specificity of 77.7%. This is the first study to explore the relationship between functional and structural changes in DPD patients and evaluate the diagnostic performance of these altered correlations to predict depression in Parkinson's disease patients. We posit that these changes in functional-structural relationships may serve as imaging biomarkers for depression in Parkinson's disease patients, potentially aiding in the classification and diagnosis of Parkinson's disease. Additionally, our findings provide functional and structural imaging evidence for exploring the neurobiological basis of depression in Parkinson's disease.

Striato-cortical functional connectivity changes in mild cognitive impairment with Lewy bodies

BACKGROUND

Functional connectivity changes in clinically overt neurodegenerative diseases such as dementia with Lewy bodies have been described, but studies on connectivity changes in the pre-dementia phase are scarce.

OBJECTIVES

We concentrated on evaluating striato-cortical functional connectivity differences between patients with Mild Cognitive Impairment with Lewy bodies and healthy controls and on assessing the relation to cognition.

METHODS

Altogether, we enrolled 77 participants (47 patients, of which 35 met all the inclusion criteria for the final analysis, and 30 age- and gender-matched healthy controls, of which 28 met all the inclusion criteria for the final analysis) to study the seed-based connectivity of the dorsal, middle, and ventral striatum. We assessed correlations between functional connectivity in the regions of between-group differences and neuropsychological scores of interest (visuospatial and executive domains z-scores).

RESULTS

Subjects with Mild Cognitive Impairment with Lewy Bodies, as compared to healthy controls, showed increased connectivity from the dorsal part of the striatum particularly to the bilateral anterior part of the temporal cortex with an association with executive functions.

CONCLUSIONS

We were able to capture early abnormal connectivity within cholinergic and noradrenergic pathways that correlated with cognitive functions known to be linked to cholinergic/noradrenergic deficits. The knowledge of specific alterations may improve our understanding of early neural changes in pre-dementia stages and enhance research of disease modifying therapy.

Identification of Parkinson's disease subtypes with distinct brain atrophy progression and its association with clinical progression

Background

Parkinson's disease (PD) patients suffer from progressive gray matter volume (GMV) loss, but whether distinct patterns of atrophy progression exist within PD are still unclear.

Objective

This study aims to identify PD subtypes with different rates of GMV loss and assess their association with clinical progression.

Methods

This study included 107 PD patients (mean age: 60.06 ± 9.98 years, 70.09% male) with baseline and ≥ 3-year follow-up structural MRI scans. A linear mixed-effects model was employed to assess the rates of regional GMV loss. Hierarchical cluster analysis was conducted to explore potential subtypes based on individual rates of GMV loss. Clinical score changes were then compared across these subtypes.

Results

Two PD subtypes were identified based on brain atrophy rates. Subtype 1 (n = 63) showed moderate atrophy, notably in the prefrontal and lateral temporal lobes, while Subtype 2 (n = 44) had faster atrophy across the brain, particularly in the lateral temporal region. Furthermore, subtype 2 exhibited faster deterioration in non-motor (MDS-UPDRS-Part Ⅰ, β = 1.26 ± 0.18, P = 0.016) and motor (MDS-UPDRS-Part Ⅱ, β = 1.34 ± 0.20, P = 0.017) symptoms, autonomic dysfunction (SCOPA-AUT, β = 1.15 ± 0.22, P = 0.043), memory (HVLT-Retention, β = -0.02 ± 0.01, P = 0.016) and depression (GDS, β = 0.26 ± 0.083, P = 0.019) compared to subtype 1.

Conclusion

The study has identified two PD subtypes with distinct patterns of atrophy progression and clinical progression, which may have implications for developing personalized treatment strategies.

Intrinsic brain functional connectivity predicts treatment-related motor complications in early Parkinson's disease patients

BACKGROUND

Treatment-related motor complications may develop progressively over the course of Parkinson's disease (PD).

OBJECTIVE

We investigated intrinsic brain networks functional connectivity (FC) at baseline in a cohort of early PD patients which successively developed treatment-related motor complications over 4 years.

METHODS

Baseline MRI images of 88 drug-naïve PD patients and 20 healthy controls were analyzed. After the baseline assessments, all PD patients were prescribed with dopaminergic treatment and yearly clinically re-assessed. At the 4-year follow-up, 36 patients have developed treatment-related motor complications (PD-Compl) whereas 52 had not (PD-no-Compl). Single-subject and group-level independent component analyses were used to investigate FC changes within the major large-scale resting-state networks at baseline. A multivariate Cox regression model was used to explore baseline predictors of treatment-related motor complications at 4-year follow-up.

RESULTS

At baseline, an increased FC in the right middle frontal gyrus within the frontoparietal network as well as a decreased connectivity in the left cuneus within the default-mode network were detected in PD-Compl compared with PD-no-Compl. PD-Compl patients showed a preserved sensorimotor FC compared to controls. FC differences were found to be independent predictors of treatment-related motor complications over time.

CONCLUSION

Our findings demonstrated that specific FC differences may characterize drug-naïve PD patients more prone to develop treatment-related complications. These findings may reflect the presence of an intrinsic vulnerability across frontal and prefrontal circuits, which may be potentially targeted as a future biomarker in clinical trials.

Imaging Biomarkers in Prodromal and Earliest Phases of Parkinson's Disease

Assessing imaging biomarker in the prodromal and early phases of Parkinson's disease (PD) is of great importance to ensure an early and safe diagnosis. In the last decades, imaging modalities advanced and are now able to assess many different aspects of neurodegeneration in PD. MRI sequences can measure iron content or neuromelanin. Apart from SPECT imaging with Ioflupane, more specific PET tracers to assess degeneration of the dopaminergic system are available. Furthermore, metabolic PET patterns can be used to anticipate a phenoconversion from prodromal PD to manifest PD. In this regard, it is worth mentioning that PET imaging of inflammation will gain significance. Molecular imaging of neurotransmitters like serotonin, noradrenaline and acetylcholine shed more light on non-motor symptoms. Outside of the brain, molecular imaging of the heart and gut is used to measure PD-related degeneration of the autonomous nervous system. Moreover, optical coherence tomography can noninvasively detect degeneration of retinal fibers as a potential biomarker in PD. In this review, we describe these state-of-the-art imaging modalities in early and prodromal PD and point out in how far these techniques can and will be used in the future to pave the way towards a biomarker-based staging of PD.

Band-Specific Altered Cortical Connectivity in Early Parkinson's Disease and its Clinical Correlates

BACKGROUND

Functional connectivity (FC) has shown promising results in assessing the pathophysiology and identifying early biomarkers of neurodegenerative disorders, such as Parkinson's disease (PD).

OBJECTIVES

In this study, we aimed to assess possible resting-state FC abnormalities in early-stage PD patients using high-density electroencephalography (EEG) and to detect their clinical relationship with motor and non-motor PD symptoms.

METHODS

We enrolled 26 early-stage levodopa naïve PD patients and a group of 20 healthy controls (HC). Data were recorded with 64-channels EEG system and a source-reconstruction method was used to identify brain-region activity. FC was calculated using the weighted phase-lag index in θ, α, and β bands. Additionally, we quantified the unbalancing between β and lower frequencies through a novel index (β-functional ratio [FR]). Statistical analysis was conducted using a network-based statistical approach.

RESULTS

PD patients showed hypoconnected networks in θ and α band, involving prefrontal-limbic-temporal and frontoparietal areas, respectively, and a hyperconnected network in the β frequency band, involving sensorimotor-frontal areas. The θ FC network was negatively related to Non-Motor Symptoms Scale scores and α FC to the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III gait subscore, whereas β FC and β-FR network were positively linked to the bradykinesia subscore. Changes in θ FC and β-FR showed substantial reliability and high accuracy, precision, sensitivity, and specificity in discriminating PD and HC.

CONCLUSIONS

Frequency-specific FC changes in PD likely reflect the dysfunction of distinct cortical networks, which occur from the early stage of the disease. These abnormalities are involved in the pathophysiology of specific motor and non-motor PD symptoms, including gait, bradykinesia, mood, and cognition. © 2023 International Parkinson and Movement Disorder Society.

Updates on brain regions and neuronal circuits of movement disorders in Parkinson's disease

Parkinson's disease (PD) is a progressive neurodegenerative disease with a global burden that affects more often in the elderly. The basal ganglia (BG) is believed to account for movement disorders in PD. More recently, new findings in the original regions in BG involved in motor control, as well as the new circuits or new nucleuses previously not specifically considered were explored. In the present review, we provide up-to-date information related to movement disorders and modulations in PD, especially from the perspectives of brain regions and neuronal circuits. Meanwhile, there are updates in deep brain stimulation (DBS) and other factors for the motor improvement in PD. Comprehensive understandings of brain regions and neuronal circuits involved in motor control could benefit the development of novel therapeutical strategies in PD.

Neural functional network of early Parkinson's disease based on independent component analysis

This work explored neural network changes in early Parkinson's disease: Resting-state functional magnetic resonance imaging was used to investigate functional alterations in different stages of Parkinson's disease (PD). Ninety-five PD patients (50 early/mild and 45 early/moderate) and 37 healthy controls (HCs) were included. Independent component analysis revealed significant differences in intra-network connectivity, specifically in the default mode network (DMN) and right frontoparietal network (RFPN), in both PD groups compared to HCs. Inter-network connectivity analysis showed reduced connectivity between the executive control network (ECN) and DMN, as well as ECN-left frontoparietal network (LFPN), in early/mild PD. Early/moderate PD exhibited decreased connectivity in ECN-LFPN, ECN-RFPN, ECN-DMN, and DMN-auditory network, along with increased connectivity in LFPN-cerebellar network. Correlations were found between ECN-DMN and ECN-LFPN connections with UPDRS-III scores in early/mild PD. These findings suggest that PD progression involves dysfunction in multiple intra- and inter-networks, particularly implicating the ECN, and a wider range of abnormal functional networks may mark the progression of the disease.

Neurovascular coupling dysfunction of visual network organization in Parkinson's disease

Parkinson's disease (PD) has been showed perfusion and neural activity alterations in specific regions, such as the motor and visual networks; however, the clinical significance of coupling changes is still unknown. To identify how neurovascular coupling changes during the pathophysiology of PD, patients and healthy controls underwent multiparametric magnetic resonance imaging to measure neural activity organization of segregation and integration using amplitude of low-frequency fluctuation (ALFF) and functional connectivity strength (FCS), and measure vascular responses using cerebral blood flow (CBF). Neurovascular coupling was calculated as the global CBF-ALFF and CBF-FCS coupling and the regional CBF/ALFF and CBF/FCS ratio. Correlations and dynamic causal modeling was then used to evaluate relationships with disease-alterations to clinical variables and information flow. Neurovascular coupling was impaired in PD with decreased global CBF-ALFF and CBF-FCS coupling, as well as decreased CBF/ALFF in the parieto-occipital cortex (dorsal visual stream) and CBF/FCS in the temporo-occipital cortex (ventral visual stream); these decouplings were associated with motor and non-motor impairments. The distinctive patterns of neurovascular coupling alterations within the dorsal and ventral visual streams of the visual system could potentially provide additional understanding into the pathophysiological mechanisms of PD.

Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight

Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.

Connectome-based fingerprint of motor impairment is stable along the course of Parkinson's disease

Functional alterations in brain connectivity have previously been described in Parkinson's disease, but it is not clear whether individual differences in connectivity profiles might be also linked to severity of motor-symptom manifestation. Here we investigated the relevance of individual functional connectivity patterns measured with resting-state fMRI with respect to motor-symptom severity in Parkinson's disease, through a whole-brain, data-driven approach (connectome-based predictive modeling). Neuroimaging and clinical data of Parkinson's disease patients from the Parkinson's Progression Markers Initiative were derived at baseline (session 1, n = 81) and at follow-up (session 2, n = 53). Connectome-based predictive modeling protocol was implemented to predict levels of motor impairment from individual connectivity profiles. The resulting predictive model comprised a network mainly involving functional connections between regions located in the cerebellum, and in the motor and frontoparietal networks. The predictive power of the model was stable along disease progression, as the connectivity within the same network could predict levels of motor impairment, even at a later stage of the disease. Finally, connectivity profiles within this network could be identified at the individual level, suggesting the presence of individual fingerprints within resting-state fMRI connectivity associated with motor manifestations in Parkinson's disease.

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00751-2.

Cortical and Subcortical Changes in MEG Activity Reflect Parkinson's Progression over a Period of 7 Years

In this study of early functional changes in Parkinson's disease (PD), we aimed to provide a comprehensive assessment of the development of changes in both cortical and subcortical neurophysiological brain activity, including their association with clinical measures of disease severity. Repeated resting-state MEG recordings and clinical assessments were obtained in the context of a unique longitudinal cohort study over a seven-year period using a multiple longitudinal design. We used linear mixed-models to analyze the relationship between neurophysiological (spectral power and functional connectivity) and clinical data. At baseline, early-stage (drug-naïve) PD patients demonstrated spectral slowing compared to healthy controls in both subcortical and cortical brain regions, most outspoken in the latter. Over time, spectral slowing progressed in strong association with clinical measures of disease progression (cognitive and motor). Global functional connectivity was not different between groups at baseline and hardly changed over time. Therefore, investigation of associations with clinical measures of disease progression were not deemed useful. An analysis of individual connections demonstrated differences between groups at baseline (higher frontal theta, lower parieto-occipital alpha2 band functional connectivity) and over time in PD patients (increase in frontal delta and theta band functional connectivity). Our results suggest that spectral measures are promising candidates in the search for non-invasive markers of both early-stage PD and of the ongoing disease process.

Normalization effect of dopamine replacement therapy on brain functional connectome in Parkinson's disease

Dopamine replacement therapy (DRT) represents the standard treatment for Parkinson's disease (PD), however, instant and long-term medication influence on patients' brain function have not been delineated. Here, a total of 97 drug-naïve patients, 43 patients under long-term DRT, and 94 normal control (NC) were, retrospectively, enrolled. Resting-state functional magnetic resonance imaging data and motor symptom assessments were conducted before and after levodopa challenge test. Whole-brain functional connectivity (FC) matrices were constructed. Network-based statistics were performed to assess FC difference between drug-naïve patients and NC, and these significant FCs were defined as disease-related connectomes, which were used for further statistical analyses. Patients showed better motor performances after both long-term DRT and levodopa challenge test. Two disease-related connectomes were observed with distinct patterns. The FC of the increased connectome, which mainly consisted of the motor, visual, subcortical, and cerebellum networks, was higher in drug-naïve patients than that in NC and was normalized after long-term DRT (p-value <.050). The decreased connectome was mainly composed of the motor, medial frontal, and salience networks and showed significantly lower FC in all patients than NC (p-value <.050). The global FC of both increased and decreased connectome was significantly enhanced after levodopa challenge test (q-value <0.050, false discovery rate-corrected). The global FC of increased connectome in ON-state was negatively associated with levodopa equivalency dose (r = -.496, q-value = 0.007). Higher global FC of the decreased connectome was related to better motor performances (r = -.310, q-value = 0.022). Our findings provided insights into brain functional alterations under dopaminergic medication and its benefit on motor symptoms.

Functional connectome automatically differentiates multiple system atrophy (parkinsonian type) from idiopathic Parkinson's disease at early stages

Differentiating the parkinsonian variant of multiple system atrophy (MSA-P) from idiopathic Parkinson's disease (IPD) is challenging, especially in the early stages. This study aimed to investigate differences and similarities in the brain functional connectomes of IPD and MSA-P patients and use machine learning methods to explore the diagnostic utility of these features. Resting-state fMRI data were acquired from 88 healthy controls, 76 MSA-P patients, and 53 IPD patients using a 3.0 T scanner. The whole-brain functional connectome was constructed by thresholding the Pearson correlation matrices of 116 regions, and topological properties were evaluated through graph theory approaches. Connectome measurements were used as features in machine learning models (random forest [RF]/logistic regression [LR]/support vector machine) to distinguish IPD and MSA-P patients. Regarding graph metrics, early IPD and MSA-P patients shared network topological properties. Both patient groups showed functional connectivity disruptions within the cerebellum-basal ganglia-cortical network, but these disconnections were mainly in the cortico-thalamo-cerebellar circuits in MSA-P patients and the basal ganglia-thalamo-cortical circuits in IPD patients. Among the connectome parameters, t tests combined with the RF method identified 15 features, from which the LR classifier achieved the best diagnostic performance on the validation set (accuracy = 92.31%, sensitivity = 90.91%, specificity = 93.33%, area under the receiver operating characteristic curve = 0.89). MSA-P and IPD patients show similar whole-brain network topological alterations. MSA-P primarily affects cerebellar nodes, and IPD primarily affects basal ganglia nodes; both conditions disrupt the cerebellum-basal ganglia-cortical network. Moreover, functional connectome parameters showed outstanding value in the differential diagnosis of early MSA-P and IPD.

Abnormal dynamic functional connectivity changes correlated with non-motor symptoms of Parkinson’s disease

Background

Non-motor symptoms are common in Parkinson’s disease (PD) patients, decreasing quality of life and having no specific treatments. This research investigates dynamic functional connectivity (FC) changes during PD duration and its correlations with non-motor symptoms.

Methods

Twenty PD patients and 19 healthy controls (HC) from PPMI dataset were collected and used in this study. Independent component analysis (ICA) was performed to select significant components from the entire brain. Components were grouped into seven resting-state intrinsic networks. Static and dynamic FC changes during resting-state functional magnetic resonance imaging (fMRI) were calculated based on selected components and resting state networks (RSN).

Results

Static FC analysis results showed that there was no difference between PD-baseline (PD-BL) and HC group. Network averaged connection between frontoparietal network and sensorimotor network (SMN) of PD-follow up (PD-FU) was lower than PD-BL. Dynamic FC analysis results suggested four distinct states, and each state’s temporal characteristics, such as fractional windows and mean dwell time, were calculated. The state 2 of our study showed positive coupling within and between SMN and visual network, while the state 3 showed hypo-coupling through all RSN. The fractional windows and mean dwell time of PD-FU state 2 (positive coupling state) were statistically lower than PD-BL. Fractional windows and mean dwell time of PD-FU state 3 (hypo-coupling state) were statistically higher than PD-BL. Outcome scales in Parkinson’s disease–autonomic dysfunction scores of PD-FU positively correlated with mean dwell time of state 3 of PD-FU.

Conclusion

Overall, our finding indicated that PD-FU patients spent more time in hypo-coupling state than PD-BL. The increase of hypo-coupling state and decrease of positive coupling state might correlate with the worsening of non-motor symptoms in PD patients. Dynamic FC analysis of resting-state fMRI can be used as monitoring tool for PD progression.

Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease

Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. Therefore, reliable biomarkers are needed. We implemented a method constructing a regional radiomics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF). We classified patients with PD and healthy individuals by using a machine learning approach in accordance with the R2SN connectome. The ALFF-based R2SN exhibited great reproducibility with different brain atlases and datasets. Great classification performances were achieved both in primary (AUC = 0.85 ± 0.02 and accuracy = 0.81 ± 0.03) and independent external validation (AUC = 0.77 and accuracy = 0.70) datasets. The discriminative R2SN edges correlated with the clinical evaluations of patients with PD. The nodes of discriminative R2SN edges were primarily located in the default mode, sensorimotor, executive control, visual and frontoparietal network, cerebellum and striatum. These findings demonstrate that ALFF-based R2SN is a robust potential neuroimaging biomarker for PD and could provide new insights into connectome reorganization in PD.

Resting-state network connectivity changes in drug-naive Parkinson's disease patients with probable REM sleep behavior disorder

Epidemiological studies have shown that Parkinson's disease (PD) patients with probable REM sleep behavior disorder (pRBD) present an increased risk of worse cognitive progression over the disease course. The aim of this study was to investigate, using resting-state functional MRI (RS-fMRI), the functional connectivity (FC) changes associated with the presence of pRBD in a cohort of newly diagnosed, drug-naive and cognitively unimpaired PD patients compared to healthy controls (HC). Fifty-six drug-naïve patients (25 PD-pRBD⁺ and 31 PD-pRBD^-) and 23 HC underwent both RS-fMRI and clinical assessment. Single-subject and group-level independent component analysis was used to analyze intra- and inter-network FC differences within the major large-scale neurocognitive networks, namely the default mode (DMN), frontoparietal (FPN), salience (SN) and executive-control (ECN) networks. Widespread FC changes were found within the most relevant neurocognitive networks in PD patients compared to HC. Moreover, PD-pRBD⁺ patients showed abnormal intrinsic FC within the DMN, ECN and SN compared to PD-pRBD^-. Finally, PD-pRBD⁺ patients showed functional decoupling between left and right FPN. In the present study, we revealed that FC changes within the most relevant neurocognitive networks are already detectable in early drug-naïve PD patients, even in the absence of clinical overt cognitive impairment. These changes are even more evident in PD patients with RBD, potentially leading to profound impairment in cognitive processing and cognitive/behavioral integration, as well as to fronto-striatal maladaptive compensatory mechanisms.

The human functional connectome in neurodegenerative diseases: relationship to pathology and clinical progression

INTRODUCTION

Neurodegenerative diseases can be considered as 'disconnection syndromes,' in which a communication breakdown prompts cognitive or motor dysfunction. Mathematical models applied to functional resting-state MRI allow for the organization of the brain into nodes and edges, which interact to form the functional brain connectome.

AREAS COVERED

The authors discuss the recent applications of functional connectomics to neurodegenerative diseases, from preclinical diagnosis, to follow up along with the progressive changes in network organization, to the prediction of the progressive spread of neurodegeneration, to stratification of patients into prognostic groups, and to record responses to treatment. The authors searched PubMed using the terms 'neurodegenerative diseases' AND 'fMRI' AND 'functional connectome' OR 'functional connectivity' AND 'connectomics' OR 'graph metrics' OR 'graph analysis.' The time range covered the past 20 years.

EXPERT OPINION

Considering the great pathological and phenotypical heterogeneity of neurodegenerative diseases, identifying a common framework to diagnose, monitor and elaborate prognostic models is challenging. Graph analysis can describe the complexity of brain architectural rearrangements supporting the network-based hypothesis as unifying pathogenetic mechanism. Although a multidisciplinary team is needed to overcome the limit of methodologic complexity in clinical application, advanced methodologies are valuable tools to better characterize functional disconnection in neurodegeneration.

Topological disruption of high-order functional networks in cognitively preserved Parkinson's disease

AIMS

This study aimed to characterize the topological alterations and classification performance of high-order functional connectivity (HOFC) networks in cognitively preserved patients with Parkinson's disease (PD), relative to low-order FC (LOFC) networks.

METHODS

The topological metrics of the constructed networks (LOFC and HOFC) obtained from fifty-one cognitively normal patients with PD and 60 matched healthy control subjects were analyzed. The discriminative abilities were evaluated using machine learning approach.

RESULTS

The HOFC networks in the PD group showed decreased segregation and integration. The normalized clustering coefficient and small-worldness in the HOFC networks were correlated to motor performance. The altered nodal centralities (distributed in the precuneus, putamen, lingual gyrus, supramarginal gyrus, motor area, postcentral gyrus and inferior occipital gyrus) and intermodular FC (frontoparietal and visual networks, sensorimotor and subcortical networks) were specific to HOFC networks. Several highly connected nodes (thalamus, paracentral lobule, calcarine fissure and precuneus) and improved classification performance were found based on HOFC profiles.

CONCLUSION

This study identified disrupted topology of functional interactions at a high level with extensive alterations in topological properties and improved differentiation ability in patients with PD prior to clinical symptoms of cognitive impairment, providing complementary insights into complex neurodegeneration in PD.

Relationship between CSF tau biomarkers and structural brain MRI measures in frontotemporal lobar degeneration

BACKGROUND

Recently in the field neurodegenerative diseases increasing attention has been pointed to CSF biomarkers and their integration with neuroimaging (1). Frontotemporal lobar degeneration (FTLD) refers to a heterogeneous group of clinical syndromes with different underlying proteinopathies including tau pathology. CSF biomarkers have been proposed as diagnostic and prognostic factors. Aim of our study was to evaluate the relationship between CSF tau biomarkers and structural MRI brain measures in FTLD.

METHODS

We included early FTLD patient. All included patients underwent lumbar puncture to evaluate amyloid, total-tau (t-tau), phospho-tau 181 (p-tau); p-tau/t-tau ratio was also calculated; brain MRI was performed to estimate whole brain volume, volume of principal deep grey matter structures and regional cortical thickness.

RESULTS

Demographic characteristics of the 28 included patients were as follows: female/male: 9/19; mean ± SD age: 68.1 ± 7.8 years. The p-tau/t-tau ratio was significantly correlated with whole brain volume (r = 0.69; p: 0.001), left putamen volume (r = 0.55 p: 0.009), left pallidum volume (r = 0.41; p: 0.01), right accumbens area (r = 0.47; p: 0.02). P-tau/t tau ratio showed also a significant correlation with cortical thickness of left temporal lobe (r = 0.74; p: 0.001) and right lateral orbital frontal cortex (r = 0.45; p: 0.03). Linear regression showed a significant relationship between p-tau/t-tau ratio and left temporal pole (p = 0.01; r²: 0.60) and brain volume (p:0.002; r²: 0.56) after controlling for age and gender.

CONCLUSIONS

Our data suggest that CSF biomarkers, especially p-tau/t-tau ratio, could play a role as prognostic factor in FTLD. Further longitudinal investigations are needed to confirm these findings.

Combined brain network topological metrics with machine learning algorithms to identify essential tremor

BACKGROUND AND OBJECTIVE

Essential tremor (ET) is a common movement syndrome, and the pathogenesis mechanisms, especially the brain network topological changes in ET are still unclear. The combination of graph theory (GT) analysis with machine learning (ML) algorithms provides a promising way to identify ET from healthy controls (HCs) at the individual level, and further help to reveal the topological pathogenesis in ET.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 101 ET and 105 HCs. The topological properties were analyzed by using GT analysis, and the topological metrics under every single threshold and the area under the curve (AUC) of all thresholds were used as features. Then a Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) were conducted to feature dimensionality reduction. Four ML algorithms were adopted to identify ET from HCs. The mean accuracy, mean balanced accuracy, mean sensitivity, mean specificity, and mean AUC were used to evaluate the classification performance. In addition, correlation analysis was carried out between selected topological features and clinical tremor characteristics.

RESULTS

All classifiers achieved good classification performance. The mean accuracy of Support vector machine (SVM), logistic regression (LR), random forest (RF), and naïve bayes (NB) was 84.65, 85.03, 84.85, and 76.31%, respectively. LR classifier achieved the best classification performance with 85.03% mean accuracy, 83.97% sensitivity, and an AUC of 0.924. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with tremor severity.

CONCLUSION

These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET from HCs but also help us to reveal the potential topological pathogenesis of ET.

Motor progression marker for newly diagnosed drug-naïve patients with Parkinson's disease: A resting-state functional MRI study

The effective early prediction of clinical outcomes of Parkinson's disease (PD) is of great significance in the implementation of appropriate interventions. We aimed to propose a method based on the use of baseline resting-state functional characteristics (i.e., fractional amplitude of low-frequency fluctuations, fALFF) to predict motor progression in PD patients. Resting-state functional magnetic resonance imaging was performed on 48 newly-diagnosed drug-naïve PD patients and 27 age- and sex- matched healthy controls (HCs). Two PD subgroups were defined with different annual increase of Unified PD Rating Scale Part III motor scores. Least absolute shrinkage and selection operator regression analysis was performed to explore the baseline region-functional indicators for PD discrimination as well as the predictors for future motor deficits. Two significant models composed of baseline fALFF values from cerebral subregions were proposed. The classification model that distinguished PD patients from HCs (area under the curve [AUC] = 0.897) showed the most significant imaging characteristics in the putamen and precentral gyrus. The other prediction model that evaluated the degree of future deterioration of motor symptoms in PD patients (AUC = 0.916) showed the most significant imaging characteristics in the superior occipital gyrus and caudate nucleus. Furthermore, the increased regional function in bilateral caudate nuclei was correlated with the lower annual increase in motor deficits in all PD patients. The caudate nucleus might be the core region responsible for future motor deficits in newly-diagnosed PD patients, which may aid the development of disease progression preventive strategies in clinical practice.

Association of Structural Measurements of Brain Reserve With Motor Progression in Patients With Parkinson Disease

BACKGROUND AND OBJECTIVES

To investigate the relationship between baseline structural measurements of brain reserve and clinical progression in Parkinson disease (PD). To further provide a possible underlying mechanism for structural measurements of brain reserve in PD, we combined functional and transcriptional data and investigated their relationship with progression-associated patterns derived from structural measurements and longitudinal clinical scores.

METHODS

This longitudinal study collected data from June 2010 to March 2019 from 2 datasets. The Parkinson's Progression Markers Initiative (PPMI) included controls and patients with newly diagnosed PD from 24 participating sites worldwide. Results were confirmed using data from the Huashan dataset (Shanghai, China), which included controls and patients with PD. Clinical symptoms were assessed with Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scores and Schwab & England activities of daily living (ADL). Both datasets were followed up to 5 years. Linear mixed-effects (LME) models were performed to examine whether changes in clinical scores over time differed as a function of brain structural measurements at baseline.

RESULTS

A total of 389 patients with PD (n = 346, age 61.3 ± 10.03, 35% female, PPMI dataset; n = 43, age 59.4 ± 7.3, 38.7% female, Huashan dataset) with T1-MRI and follow-up clinical assessments were included in this study. Results of LME models revealed significant interactions between baseline structural measurements of subcortical regions and time on longitudinal deterioration of clinical scores (MDS-UPDRS Part II, absolute β > 0.27; total MDS-UPDRS scores, absolute β > 1.05; postural instability-gait difficulty (PIGD) score, absolute β > 0.03; Schwab & England ADL, absolute β > 0.59; all p < 0.05, false discovery rate corrected). The interaction of baseline structural measurements of subcortical regions and time on longitudinal deterioration of the PIGD score was replicated using data from Huashan Hospital. Furthermore, the β-coefficients of these interactions recapitulated the spatial distribution of dopaminergic, metabolic, and structural changes between patients with PD and normal controls and the spatial distribution of expression of the α-synuclein gene (SNCA).

DISCUSSION

Patients with PD with greater brain resources (that is, higher deformation-based morphometry values) had greater compensatory capacity, which was associated with slower rates of clinical progression. This knowledge could be used to stratify and monitor patients for clinical trials.

Comparative Study on Topological Properties of the Whole-Brain Functional Connectome in Idiopathic Rapid Eye Movement Sleep Behavior Disorder and Parkinson’s Disease Without RBD

Purpose

Idiopathic rapid eye movement Sleep Behavior Disorder (iRBD) is considered as a prodromal and most valuable warning symptom for Parkinson’s disease (PD). Although iRBD and PD without RBD (nRBD-PD) are both α-synucleinopathies, whether they share the same neurodegeneration process is not clear enough. In this study, the pattern and extent of neurodegeneration were investigated and compared between early-stage nRBD-PD and iRBD from the perspective of whole-brain functional network changes.

Methods

Twenty-one patients with iRBD, 23 patients with early-stage nRBD-PD, and 22 matched healthy controls (HCs) were enrolled. Functional networks were constructed using resting-state functional MRI (fMRI) data. Network topological properties were analyzed and compared among groups by graph theory approaches. Correlation analyses were performed between network topological properties and cognition in the iRBD and nRBD-PD groups.

Results

Both patients with iRBD and patients with early-stage nRBD-PD had attention, executive function, and some memory deficits. On global topological organization, iRBD and nRBD-PD groups still presented small-worldness, but both groups exhibited decreased global/local efficiency and increased characteristic path length. On regional topological organization, compared with HC, nRBD-PD presented decreased nodal efficiency, decreased degree centrality, and increased nodal shortest path length, while iRBD presented decreased nodal efficiency and nodal shortest path. For iRBD, brain regions with decreased nodal efficiency were included in the corresponding regions of nRBD-PD. Nodal shortest path changes were significantly different in terms of brain regions and directions between nRBD-PD and iRBD. Attention deficits were correlated with local topological properties of the occipital lobe in both iRBD and nRBD-PD groups.

Conclusion

Both global and local efficiency of functional networks declined in nRBD-PD and iRBD groups. The overlaps and differences in local topological properties between nRBD-PD and iRBD indicate that iRBD not only shares functional changes of PD but also presents distinct features.

Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis

Parkinson's disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.

Altered Intra- and Inter-Network Connectivity in Drug-Naïve Patients With Early Parkinson’s Disease

The aim of our study was to investigate differences in whole brain connectivity at different levels between drug-naïve individuals with early Parkinson’s disease (PD) and healthy controls (HCs). Resting-state functional magnetic resonance imaging data were collected from 47 patients with early-stage, drug-naïve PD and 50 HCs. Functional brain connectivity was analyzed at the integrity, network, and edge levels; UPDRS-III, MMSE, MOCA, HAMA, and HAMD scores, reflecting the symptoms of PD, were collected for further regression analysis. Compared with age-matched HCs, reduced functional connectivity were mainly observed in the visual (VSN), somatomotor (SMN), limbic (LBN), and deep gray matter networks (DGN) at integrity level [p < 0.05, false discovery rate (FDR) corrected]. Intra-network analysis indicated decreased functional connectivity in DGN, SMN, LBN, and ventral attention networks (VAN). Inter-network analysis indicated reduced functional connectivity in nine pairs of resting-state networks. At the edge level, the LBN was the center of abnormal functional connectivity (p < 0.05, FDR corrected). MOCA score was associated with the intra-network functional connectivity strength (FC) of the DGN, and inter-network FC of the DGN-VAN. HAMA and HAMD scores were associated with the FC of the SMN and DGN, and either the LBN or VAN, respectively. We demonstrated variations in whole brain connections of drug-naïve patients with early PD. Major changes involved the SMN, DGN, LBN, and VSN, which may be relevant to symptoms of early PD. Additionally, our results support PD as a disconnection syndrome.