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Stroke is one of the leading causes of adult disability affecting millions of people worldwide. Post‐stroke cognitive and motor impairments diminish quality of life and functional independence. There is an increased risk of having a second stroke and developing secondary conditions with long‐term social and economic impacts. With increasing number of stroke incidents, shortage of medical professionals and limited budgets, health services are struggling to provide a care that can break the vicious cycle of stroke. Effective post‐stroke recovery hinges on holistic, integrative and personalized care starting from improved diagnosis and treatment in clinics to continuous rehabilitation and support in the community. To improve stroke care pathways, there have been growing efforts in discovering biomarkers that can provide valuable insights into the neural, physiological and biomechanical consequences of stroke and how patients respond to new interventions. In this review paper, we aim to summarize recent biomarker discovery research focusing on three modalities (brain imaging, blood sampling and gait assessments), look at some established and forthcoming biomarkers, and discuss their usefulness and complementarity within the context of comprehensive stroke care. We also emphasize the importance of biomarker guided personalized interventions to enhance stroke treatment and post‐stroke recovery. [ABSTRACT FROM AUTHOR]

Objective: Parkinson's disease (PD) has a heterogeneous cognitive profile and its pathophysiology has not been completely clarified. In this study, functional connectivity (FC) changes were investigated in PD patients and in PD subgroups classified according to cognitive and motor performance, using resting state functional magnetic resonance imaging (rs-fMRI) data. Methods: 55 PD patients diagnosed with PD according to the UK Brain Bank Diagnostic Criteria and 24 healthy controls (CH) matched for age, education and gender were enrolled in the study. Resting state fMRI data were collected using a 3T MR imaging device (Phillips, Achieva, The Netherlands) at Istanbul Medical Faculty. PD subgroups were generated using a clustering algorithm according to the scores of UPDRS-III, Stroop and Benton judgment of line orientation tests. Seed-based FC analysis was performed using the AAL3 atlas covering 112 seeds and CONN toolbox. Network-Based Statistics (NBS) method was used in the FC analysis to compare HC with the PD and with the PD sub-groups. Results: As a result of FC analysis, there was a significant decrease in FC between the somatomotor network (SMN) and visual regions in PD patients compared to the SC group (p<0.05, FWE-corrected). In the comparisons of PD subgroups and HC, PD patients with worse motor performance showed reduced FC in the SMN (p<0.05, FWE-adjusted); PD patients with worse visuospatial performance showed FC alterations at visual-SMN cortical connections in a subnetwork covering large visual regions (p<0.05, FWE-corrected). Conclusion: In our study, a significant decrease in FC was detected between SMN and visual regions in PD. These FC alterations indicate impaired visual-motor integration in PD. Moreover, our results also showed that besides the motor impairment in PD, the visuospatial impairment observed in PD causes increased visual-SMN disconnection. This study supported by TUBITAK #115S219. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurodegenerative disorder that has affected more than 4 million people in the past 50 years. While Parkinson's disease is generally known for motor symptoms such as tremor, posture and gait disturbances, and muscle stiffness, cog-nitive dysfunction has also been indicated at the early stages of the disease. Parkinson's disease dementia (PDD) affects daily life by disrupting memory, fluent speech, visuospatial and visual-perceptual functions. Parkinson's disease mild cognitive impairment (PD-MCI), on the other hand, is defined as a transitional stage between cognitively normal (PD-CN) and PDD. PD-MCI is seen in 27% of PD patients, and MCI is an important determinant for the development of dementia. In the past years, studies have been carried out to determine the diagnostic criteria of PD-MCI, which remain subjective due to the qualitative measurements. Magnetic resonance (MR) imaging has been commonly used in the diagnosis and follow-up of neurodegenerative diseases. Multimodality MR imaging provides chemical, functional and anatomical information of the underlying tissue of interest. MR spectroscopic imaging provides noninvasive information regarding the biochemistry of the tissue. Arterial spin labeling MR imaging is used to acquire cerebral blood flow and arterial blood volume maps without the need of using contrast agents. In this talk, several of our studies on the detection of PD-MCI and PDD with multimodal MR imaging methods and machine learning will be discussed. [ABSTRACT FROM AUTHOR]

Objective: Parkinson's disease (PD) is a neurodegenerative disease characterized by clinical motor symptoms. In addition to motor symptoms, cognitive impairment is one of the most common non-motor symptom observed in PD. In this study, functional connectivity (FC) changes in PD were investigated by performing a seed-based analysis using resting-state functional magnetic resonance imaging (rs-fMRI) data to identify neuroimaging patterns related to motor and cognitive impairment in PD. Methods: 55 PD patients diagnosed with PD according to the UK Brain Bank Diagnostic Criteria and 24 healthy controls (HC) matched for age, education, and gender were included in the study. Resting-state fMRI was acquired on a 3T MR (Phillips, Achieva, The Netherlands) at Istanbul Medical Faculty. To evaluate motor and executive, visuospatial cognitive functions of PD patients, UPDRS-III, Stroop, and Benton Judgment of Line Orientation tests were used, respectively. Seed-based FB analysis was performed using the CONN toolbox. Network-Based Statistics (NBS) method was carried out using the AAL3 atlas including 112 seeds to compare HC with the PH and with the PH sub-groups classified in terms of motor and cognitive performances. Results: Compared to the HC, the PD group showed decreased FC in the 78 connections and 21 cortical regions including mostly the sensorimotor and visual areas (p<0.05, FWE-corrected). Compared to the HC, the PD sub-group with low motor performance showed decreased FC in the 22 connections (p<0.05, FWE-corrected); the PD sub-group with low visuospatial performance showed decreased FC in the 38 connections (p<0.05, FWE-corrected). Conclusion: In our study, a significant decrease in FC was detected between the sensorimotor and the visual network, indicating impaired visual-motor integration in PD. Furthermore, our results showed that FC alterations between the sensorimotor and visual regions increase associated with visuospatial cognitive impairment in PD. [ABSTRACT FROM AUTHOR]

Objective: Parkinson's disease (PD) is a progressive neurodegenerative disease, which is characterized by severe motor symptoms such as bradykinesia, tremor, and rigidity. Degeneration is characterized by progressive loss of dopaminergic neurons in the substantia nigra. Mild Cognitive Impairment (PD-MCI), emerges as one of the major risk factors for PD Dementia (PDD). Revealing the markers associated with cognitive impairment with neuroimaging may play a critical role in the early diagnosis of the disease and early treatment. Methods: 28 PD Patients with Mild Cognitive Impairment (PD-MCI) and 27 cognitively normal PD patients (PD-CN) who were statistically similar in terms of mean education years, age, and gender were included. Functional magnetic resonance imaging (fMRI) data were collected with a 3T MRI (Phillips, Achieva, The Netherlands). Fractional amplitudes of low-frequency fluctuations (fALFF) of resting-state blood-oxygenlevel-dependent (BOLD) signal were calculated at the voxel level. after preprocessing steps with CONN (https://web.conntoolbox. org/) software. Clusters with significant differences in fALFF values between the groups were calculated using the general linear model with a statistical threshold of puncorr< 0.001 at the voxel level and pFDR <0.05 corrected at the cluster level. Clusters with 100 or more voxels were evaluated. Results: In the PD-MCI group, fALFF values were found to be significantly lower in the temporal pole and insular cortex in the left hemisphere and in the insular cortex in the right hemisphere, when compared to the PD-CN group. Conclusion: In the PD-MCI group, compared to the PD-CN group, reduction of the D2 receptor availability in the bilateral insula was previously demonstrated by Positron Emission Tomography (PET). This finding in PET, which requires the use of radioactive agents, has not yet been demonstrated in fMRI. In this study, fALFF decreases, observed in bilateral insula, highlight the importance of insular circuits in cognitive impairment in PD, and also sheds light on the neurochemical mechanisms of BOLD signal. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to classify Parkinson's disease mild cognitive impairment (PD-MCI), cognitively normal Parkinson's disease (PD-CN) and healthy control (HC) groups based on multimodal magnetic resonance imaging (MRI) using machine learning methods. Methods: 33 PD-MCI, 27 PD-CN and 17 HC participated in this study. The participants were diagnosed by neurologists according to the neuropsychological test and physical examination results. MRI data was obtained at a 3T Philips clinical MR system using a 32-channel head coil. Mean cerebral blood flow (CBF), arterial blood volume (aBV) and bolus arrival time (BAT) maps obtained from arterial spin labeling MRI (ASL-MRI), fractional anisotropy (FA) and mean diffusivity (MD) maps obtained from diffusion tensor imaging (DTI), and metabolite peak ratios obtained from proton MR spectroscopic imaging (1H-MRSI) at various brain regions were used as features. Various machine learning methods were employed with appropriate hyperparameters. In addition, feature selection algorithms and dimension reduction techniques such as principal component analysis (PCA) and non-negative matrix factorization (NNMF) were assessed. Features having high correlation with each other were eliminated. Results: Removing highly correlated features increased the model performance. The subset of features selected by the randomized logistic regression and leave one out cross-validation (RLR-LOOCV) method contained 10% of all features from all the MRI modalities. The classification accuracies were 77% for PD-MCI versus HC, 80% for PD-MCI versus PD-CN, and 71% for PD-CN versus HC. Conclusion: Machine learning based on multimodal MRI might be helpful in early diagnosis of PD-MCI. Future studies aim to improve the classification of PD-MCI in a larger patient cohort. This study has been supported by TÜBITAK #115S219 and Ministry of Development #2010K120330. [ABSTRACT FROM AUTHOR]

Magnetic resonance (MR) imaging has been commonly used in diagnosis and follow-up of neurodegenerative diseases. Multimodality MR imaging provides chemical, functional and anatomical information of the underlying tissue of interest. MR spectroscopic imaging provides noninvasive information regarding the biochemistry of the tissue. Arterial spin labeling MR imaging is used to acquire cerebral blood flow and arterial blood volume maps without the need of using contrast agents. Resting state functional MR imaging provides brain activation connectivity information during rest. Diffusion MR imaging tracks the water movement along the white matter tracts, providing diffusivity and anisotropy information of water movement. In this talk, information will be shared regarding multimodality MR imaging techniques and biomarkers of especially mild cognitive impairment in Parkinson's disease. [ABSTRACT FROM AUTHOR]

Objective: Parkinson's disease-mild cognitive impairment (PD-MCI) has a higher risk of developing PD dementia (PDD) and a reliable biomarker for the diagnosis of PD-MCI has not developed yet. In this study, in order to develop discriminative neurobiological parameters for PD-MCI, functional and structural differences among PD-MCI, cognitively normal PD (CN-PD) and healthy controls (HC) were investigated using resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI) modalities. Methods: 60 PD (27 PH-CN, 33 PD-MCI) patients and 17 HC were included in this study. MR imaging was performed on 3T Phillips MRI scanner. Resting state networks (RSN) were obtained by using independent component analysis (ICA) in Group-ICA fMRI Toolbox. Expression scores specific for each subject were fed into logistic regression to obtain RSNs discriminating three groups from each other. ROI based approach was used for analysis of DTI data and fractional anisotropy (FA) values of each subject were compared with one-way ANOVA. Results: Logistic regression analysis yielded maximum separation of between PD-MCI and PD-CN groups with default mode network (DMN) - posterior cingulate component with an overall accuracy of 63,3% (χ2=6.945, df=1, p=0.008). Sensorymotor and visual network provided maximum separation between PDMCI and HC groups with an overall accuracy of 80% (χ2=9.514, df=2, p=0.009). FA values differed among three groups in the superior longitudinal fasciculus (SLF) (p=0.028) and SLF-temporal part (p=0.026) and FA values in these ROIs significantly decreased in PD-MCI compared to HC. Conclusion: In previous studies, lower connectivity in DMN and decreased FA values in SLF have been demonstrated in patients with PDD. In this regard, our similar results in PDMCI may be an indicator for cognitive decline in PD. Additionally, lower connectivity in visual network could be associated with visuospatial impairment in PD-MCI. Supported by TUBITAK #115S219 and IU-BAP #21336. [ABSTRACT FROM AUTHOR]