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Deferm W, Tang T, Moerkerke M, Daniels N, Steyaert J, Alaerts K, Ortibus E, Naulaers G, Boets B. Subtle microstructural alterations in white matter tracts involved in socio-emotional processing after very preterm birth. Neuroimage Clin 2024; 41:103580. [PMID: 38401459 PMCID: PMC10944182 DOI: 10.1016/j.nicl.2024.103580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/10/2024] [Accepted: 02/10/2024] [Indexed: 02/26/2024]
Abstract
Children born very preterm (VPT, < 32 weeks of gestation) have an increased risk of developing socio-emotional difficulties. Possible neural substrates for these socio-emotional difficulties are alterations in the structural connectivity of the social brain due to premature birth. The objective of the current study was to study microstructural white matter integrity in VPT versus full-term (FT) born school-aged children along twelve white matter tracts involved in socio-emotional processing. Diffusion MRI scans were obtained from a sample of 35 VPT and 38 FT 8-to-12-year-old children. Tractography was performed using TractSeg, a state-of-the-art neural network-based approach, which offers investigation of detailed tract profiles of fractional anisotropy (FA). Group differences in FA along the tracts were investigated using both a traditional and complementary functional data analysis approach. Exploratory correlations were performed between the Social Responsiveness Scale (SRS-2), a parent-report questionnaire assessing difficulties in social functioning, and FA along the tract. Both analyses showed significant reductions in FA for the VPT group along the middle portion of the right SLF I and an anterior portion of the left SLF II. These group differences possibly indicate altered white matter maturation due to premature birth and may contribute to altered functional connectivity in the Theory of Mind network which has been documented in earlier work with VPT samples. Apart from reduced social motivation in the VPT group, there were no significant group differences in reported social functioning, as assessed by SRS-2. We found that in the VPT group higher FA values in segments of the left SLF I and right SLF II were associated with better social functioning. Surprisingly, the opposite was found for segments in the right IFO, where higher FA values were associated with worse reported social functioning. Since no significant correlations were found for the FT group, this relationship may be specific for VPT children. The current study overcomes methodological limitations of previous studies by more accurately segmenting white matter tracts using constrained spherical deconvolution based tractography, by applying complementary tractometry analysis approaches to estimate changes in FA more accurately, and by investigating the FA profile along the three components of the SLF.
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Affiliation(s)
- Ward Deferm
- Center for Developmental Psychiatry, KU Leuven, Belgium.
| | - Tiffany Tang
- Center for Developmental Psychiatry, KU Leuven, Belgium
| | | | - Nicky Daniels
- Neuromotor Rehabilitation Research Group, KU Leuven, Belgium
| | - Jean Steyaert
- Center for Developmental Psychiatry, KU Leuven, Belgium; Child Psychiatry, UZ Leuven, Belgium
| | - Kaat Alaerts
- Neuromotor Rehabilitation Research Group, KU Leuven, Belgium
| | | | - Gunnar Naulaers
- Neonatal Intensive Care Unit - Neonatology, UZ Leuven, Belgium; UZ Leuven & Center for Developmental Disorders, Belgium
| | - Bart Boets
- Center for Developmental Psychiatry, KU Leuven, Belgium
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Zeng Q, Yu J, Hu Q, Yin K, Li Q, Huang J, Xie L, Wang J, Zhang C, Wang J, Zhang J, Feng Y. Investigation into white matter microstructure differences in visual training by using an automated fiber tract subclassification segmentation quantification method. Neurosci Lett 2024; 821:137574. [PMID: 38036084 DOI: 10.1016/j.neulet.2023.137574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/25/2023] [Accepted: 11/27/2023] [Indexed: 12/02/2023]
Abstract
Visual training has emerged as a useful framework for investigating training-related brain plasticity, a highly complex task involving the interaction of visual orientation, attention, reasoning, and cognitive functions. However, the effects of long-term visual training on microstructural changes within white matter (WM) is poorly understood. Therefore, a set of visual training programs was designed, and automated fiber tract subclassification segmentation quantification based on diffusion magnetic resonance imaging was performed to obtain the anatomical changes in the brains of visual trainees. First, 40 healthy matched participants were randomly assigned to the training group or the control group. The training group underwent 10 consecutive weeks of visual training. Then, the fiber tracts of the subjects were automatically identified and further classified into fiber clusters to determine the differences between the two groups on a detailed scale. Next, each fiber cluster was divided into segments that can analyze specific areas of a fiber cluster. Lastly, the diffusion metrics of the two groups were comparatively analyzed to delineate the effects of visual training on WM microstructure. Our results showed that there were significant differences in the fiber clusters of the cingulate bundle, thalamus frontal, uncinate fasciculus, and corpus callosum between the training group compared and the control group. In addition, the training group exhibited lower mean fractional anisotropy, higher mean diffusivity and radial diffusivity than the control group. Therefore, the long-term cognitive activities, such as visual training, may systematically influence the WM properties of cognition, attention, memory, and processing speed.
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Affiliation(s)
- Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Jiangli Yu
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Qiming Hu
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Kuiying Yin
- Nanjing Research Institute of Electronic Technology, Nanjing 210012, China
| | - Qixue Li
- Nanjing Research Institute of Electronic Technology, Nanjing 210012, China
| | - Jiahao Huang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Lei Xie
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Jingqiang Wang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Chengzhe Zhang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Jiafeng Wang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Jiawei Zhang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China.
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Filimonova E, Pashkov A, Moysak G, Martirosyan A, Zaitsev B, Rzaev J. Diffusion tensor imaging reveals distributed white matter abnormalities in primary trigeminal neuralgia: Tract-based spatial statistics study. Clin Neurol Neurosurg 2024; 236:108080. [PMID: 38113657 DOI: 10.1016/j.clineuro.2023.108080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 11/30/2023] [Accepted: 12/06/2023] [Indexed: 12/21/2023]
Abstract
BACKGROUND Primary trigeminal neuralgia (PTN) is a prevalent chronic pain disorder whose pathogenesis is not limited to the trigeminal system. Despite the significant advances in uncovering underlying mechanisms, there is a paucity of comprehensive and consistent data regarding the role of white matter throughout the entire brain in PTN. METHODS We performed a prospective case-control study. Sixty patients with PTN and 28 age- and sex-matched healthy controls were evaluated using diffusion tensor imaging (DTI). A tract-based spatial statistical approach was performed to investigate white matter impairment in patients with PTN with several metrics, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD). Additionally, ROI-based analysis was performed for each white matter tract to compare FA values between groups with correction for patient age and sex. Correlations between DTI data and nerve root compression severity, as well as pain severity, were also evaluated in patients with PTN. RESULTS Our analysis demonstrated a widespread and symmetrical reduction in FA values among TN patients when compared to the control group (p < 0.05). Specifically, this FA decrease was predominantly observed in regions such as the corona radiata, internal capsule, optic radiation, and thalami, as well as structures within the posterior fossa, notably the cerebellar peduncles. No statistically significant differences were found between patients and the control group during the MD, AD and RD map analyses. ROI-based analysis did not reveal statistically significant changes in FA values in white matter tracts (p > 0.05 in all comparisons, FDR-corrected); however, there were trends towards FA value decreases in the internal capsule (p = 0.08, FDR-corrected) and inferior fronto-occipital fasciculus (p = 0.09, FDR-corrected). CONCLUSIONS Our findings indicate the presence of microstructural abnormalities in white matter among individuals with primary trigeminal neuralgia, which may potentially play a role in the development and progression of the condition.
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Affiliation(s)
- Elena Filimonova
- FSBI "Federal Center of Neurosurgery", Novosibirsk, Russia; Department of Neurosurgery, Novosibirsk State Medical University, Novosibirsk, Russia.
| | - Anton Pashkov
- FSBI "Federal Center of Neurosurgery", Novosibirsk, Russia; Department of Neurosurgery, Novosibirsk State Medical University, Novosibirsk, Russia; Department of Data Collection and Processing Systems, Novosibirsk State Technical University, Novosibirsk, Russia
| | - Galina Moysak
- FSBI "Federal Center of Neurosurgery", Novosibirsk, Russia; Department of Neurosurgery, Novosibirsk State Medical University, Novosibirsk, Russia; Department of Neuroscience, Institute of Medicine and Psychology, Novosibirsk State University, Novosibirsk, Russia
| | | | - Boris Zaitsev
- FSBI "Federal Center of Neurosurgery", Novosibirsk, Russia
| | - Jamil Rzaev
- FSBI "Federal Center of Neurosurgery", Novosibirsk, Russia; Department of Neurosurgery, Novosibirsk State Medical University, Novosibirsk, Russia; Department of Neuroscience, Institute of Medicine and Psychology, Novosibirsk State University, Novosibirsk, Russia
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Jellinger KA. Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks. Int J Mol Sci 2023; 25:498. [PMID: 38203667 PMCID: PMC10778722 DOI: 10.3390/ijms25010498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 12/11/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024] Open
Abstract
Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and β-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.
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Affiliation(s)
- Kurt A Jellinger
- Institute of Clinical Neurobiology, Alberichgasse 5/13, A-1150 Vienna, Austria
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Yu Z, Pang H, Yu H, Wu Z, Ding Z, Fan G. Segmental disturbance of white matter microstructure in predicting mild cognitive impairment in idiopathic Parkinson's disease: An individualized study based on automated fiber quantification tractography. Parkinsonism Relat Disord 2023; 115:105802. [PMID: 37734997 DOI: 10.1016/j.parkreldis.2023.105802] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 08/01/2023] [Accepted: 08/05/2023] [Indexed: 09/23/2023]
Abstract
PURPOSE The neurobiological mechanisms and an early identification of MCI in idiopathic Parkinson's disease (IPD) remain unclear. To investigate the abnormalities of types of white matter (WM) fiber tracts segmentally and establish reliable indicator in IPD-MCI. METHODS Forty IPD with normal cognition (IPD-NCI), thirty IPD-MCI, and thirty healthy controls were included. Automated fiber quantification was applied to extract the fractional anisotropy (FA) and mean diffusivity (MD) values at 100 locations along the major fibers. Partial correlation was performed between diffusion values and cognitive performance. Furthermore, machine learning analyses were conducted to determine the imaging biomarker of MCI. Permutation tests were performed to evaluate the pointwise differences under the FWE correction. RESULTS IPD-MCI had similar but more severe and widespread WM degeneration in the association, projection, and commissural fibers compared with IPD-NCI. Meanwhile, IPD-MCI showed distinct degeneration pattern in the association fibers. The FA of the anterior segment of right inferior fronto-occipital fasciculus (IFOF) was positively correlated with MoCA (P < 0.05) and executive function (P < 0.05). The MD of the middle and posterior segment of left superior longitudinal fasciculus (SLF) was negatively correlated with MoCA P < 0.05), executive (P < 0.05), visuospatial function (P < 0.05). Furthermore, the AUC of support vector machine model was 0.80 in the validation dataset. The FA of anterior segment of right IFOF contribute the most. CONCLUSION This study demonstrated that regional tract-specific microstructural degeneration, especially the association fibers, can be used to predict MCI in IPD. Especially, the right IFOF may be a significant imaging biomarker in predicting IPD with MCI.
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Affiliation(s)
- Ziyang Yu
- School of Medicine, Xiamen University, Xiamen, Fujian Province, China; Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China.
| | - Huize Pang
- Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China.
| | - Hongmei Yu
- Department of Neurology, First Affiliated Hospital of China Medical University, Shenyang, China.
| | - Ziqian Wu
- School of Medicine, Xiamen University, Xiamen, Fujian Province, China.
| | - Zhi Ding
- School of Medicine, Xiamen University, Xiamen, Fujian Province, China.
| | - Guoguang Fan
- Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China.
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Elmers J, Colzato LS, Akgün K, Ziemssen T, Beste C. Neurofilaments - Small proteins of physiological significance and predictive power for future neurodegeneration and cognitive decline across the life span. Ageing Res Rev 2023; 90:102037. [PMID: 37619618 DOI: 10.1016/j.arr.2023.102037] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/15/2023] [Accepted: 08/17/2023] [Indexed: 08/26/2023]
Abstract
Neurofilaments (NFs) are not only important for axonal integrity and nerve conduction in large myelinated axons but they are also thought to be crucial for receptor and synaptic functioning. Therefore, NFs may play a critical role in cognitive functions, as cognitive processes are known to depend on synaptic integrity and are modulated by dopaminergic signaling. Here, we present a theory-driven interdisciplinary approach that NFs may link inflammation, neurodegeneration, and cognitive functions. We base our hypothesis on a wealth of evidence suggesting a causal link between inflammation and neurodegeneration and between these two and cognitive decline (see Fig. 1), also taking dopaminergic signaling into account. We conclude that NFs may not only serve as biomarkers for inflammatory, neurodegenerative, and cognitive processes but also represent a potential mechanical hinge between them, moreover, they may even have predictive power regarding future cognitive decline. In addition, we advocate the use of both NFs and MRI parameters, as their synthesis offers the opportunity to individualize medical treatment by providing a comprehensive view of underlying disease activity in neurological diseases. Since our society will become significantly older in the upcoming years and decades, maintaining cognitive functions and healthy aging will play an important role. Thanks to technological advances in recent decades, NFs could serve as a rapid, noninvasive, and relatively inexpensive early warning system to identify individuals at increased risk for cognitive decline and could facilitate the management of cognitive dysfunctions across the lifespan.
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Affiliation(s)
- Julia Elmers
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Germany; Center of Clinical Neuroscience, Department of Neurology, University Hospital Carl Gustav Carus, TU Dresden, Germany
| | - Lorenza S Colzato
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Germany; Cognitive Psychology, Faculty of Psychology, Shandong Normal University, Jinan, China.
| | - Katja Akgün
- Center of Clinical Neuroscience, Department of Neurology, University Hospital Carl Gustav Carus, TU Dresden, Germany
| | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Department of Neurology, University Hospital Carl Gustav Carus, TU Dresden, Germany
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Germany; Cognitive Psychology, Faculty of Psychology, Shandong Normal University, Jinan, China.
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Corp DT, Morrison-Ham J, Jinnah HA, Joutsa J. The functional anatomy of dystonia: Recent developments. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2023; 169:105-136. [PMID: 37482390 DOI: 10.1016/bs.irn.2023.04.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
While dystonia has traditionally been viewed as a disorder of the basal ganglia, the involvement of other key brain structures is now accepted. However, just what these structures are remains to be defined. Neuroimaging has been an especially valuable tool in dystonia, yet traditional cross-sectional designs have not been able to separate causal from compensatory brain activity. Therefore, this chapter discusses recent studies using causal brain lesions, and animal models, to converge upon the brain regions responsible for dystonia with increasing precision. This evidence strongly implicates the basal ganglia, thalamus, brainstem, cerebellum, and somatosensory cortex, yet shows that different types of dystonia involve different nodes of this brain network. Nearly all of these nodes fall within the recently identified two-way networks connecting the basal ganglia and cerebellum, suggesting dysfunction of these specific pathways. Localisation of the functional anatomy of dystonia has strong implications for targeted treatment options, such as deep brain stimulation, and non-invasive brain stimulation.
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Affiliation(s)
- Daniel T Corp
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia; Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Boston, MA, United States.
| | - Jordan Morrison-Ham
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
| | - H A Jinnah
- Departments of Neurology, Human Genetics, and Pediatrics, Atlanta, GA, United States
| | - Juho Joutsa
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Boston, MA, United States; Turku Brain and Mind Center, Clinical Neurosciences, University of Turku, Turku, Finland; Turku PET Centre, Neurocenter, Turku University Hospital, Turku, Finland
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Robertson JW, Aristi G, Hashmi JA. White matter microstructure predicts measures of clinical symptoms in chronic back pain patients. Neuroimage Clin 2023; 37:103309. [PMID: 36621020 PMCID: PMC9850203 DOI: 10.1016/j.nicl.2022.103309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 11/30/2022] [Accepted: 12/26/2022] [Indexed: 12/28/2022]
Abstract
Chronic back pain (CBP) has extensive clinical and social implications for its sufferers and is a major source of disability. Chronic pain has previously been shown to have central neural factors underpinning it, including the loss of white matter (WM), however traditional methods of analyzing WM microstructure have produced mixed and unclear results. To better understand these factors, we assessed the WM microstructure of 50 patients and 40 healthy controls (HC) using diffusion-weighted imaging. The data were analyzed using fixel-based analysis (FBA), a higher-order diffusion modelling technique applied to CBP for the first time here. Subjects also answered questionnaires relating to pain, disability, catastrophizing, and mood disorders, to establish the relationship between fixelwise metrics and clinical symptoms. FBA determined that, compared to HC, CBP patients had: 1) lower fibre density (FD) in several tracts, specifically the right anterior and bilateral superior thalamic radiations, right spinothalamic tract, right middle cerebellar peduncle, and the body and splenium of corpus callosum; 2) higher FD in the genu of corpus callosum; and 3) lower FDC - a combined fibre density and cross-section measure - in the bilateral spinothalamic tracts and right anterior thalamic radiation. Exploratory correlations showed strong negative relationships between fixelwise metrics and clinical questionnaire scores, especially pain catastrophizing. These results have important implications for the intake and processing of sensory data in CBP that warrant further investigation.
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Affiliation(s)
- Jason W Robertson
- Department of Anesthesia, Pain Management and Perioperative Medicine, Dalhousie University, 1276 South Park St., Halifax, Nova Scotia B3H 2Y9, Canada; Nova Scotia Health Authority, 1276 South Park St., Halifax, Nova Scotia B3H 2Y9, Canada.
| | - Guillermo Aristi
- Department of Anesthesia, Pain Management and Perioperative Medicine, Dalhousie University, 1276 South Park St., Halifax, Nova Scotia B3H 2Y9, Canada; Nova Scotia Health Authority, 1276 South Park St., Halifax, Nova Scotia B3H 2Y9, Canada
| | - Javeria A Hashmi
- Department of Anesthesia, Pain Management and Perioperative Medicine, Dalhousie University, 1276 South Park St., Halifax, Nova Scotia B3H 2Y9, Canada; Nova Scotia Health Authority, 1276 South Park St., Halifax, Nova Scotia B3H 2Y9, Canada.
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Hu Y, Wu Y, Su H, Tu J, Zeng L, Lei J, Xia L. Exploring the relationship between brain white matter change and higher degree of invisible hand tremor with computer technology. Technol Health Care 2022; 31:921-931. [PMID: 36442160 DOI: 10.3233/thc-220361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
BACKGROUND: At present, the clinical diagnosis of white matter change (WMC) patients depends on cranial magnetic resonance imaging (MRI) technology. This diagnostic method is costly and does not allow for large-scale screening, leading to delays in the patient’s condition due to inability to receive timely diagnosis. OBJECTIVE: To evaluate whether the burden of WMC is associated with the degree of invisible hand tremor in humans. METHODS: Previous studies have shown that tremor is associated with WMC, however, tremor does not always have imaging of WMC. Therefore, to confirm that the appearance of WMC causes tremor, which are sometimes invisible to the naked eye, we achieved an optical-based computer-aided diagnostic device by detecting the invisible hand tremor, and we proposed a calculation method of WMC volume by using the characteristics of MRI images. RESULTS: Statistical analysis results further clarified the relationship between WMC and tremor, and our devices are validated for the detection of tremors with WMC. CONCLUSIONS: The burden of WMC volume is positive factor for degree of invisible hand tremor in the participants without visible hand tremor. Detection technology provides a more convenient and low-cost evaluating method before MRI for tremor diseases.
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Affiliation(s)
- Yang Hu
- Department of Cardiovascular Medicine, The Second Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Yanqing Wu
- Department of Cardiovascular Medicine, The Second Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Hai Su
- Department of Cardiovascular Medicine, The Second Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Jianglong Tu
- Department of Nephrology Medicine, The Second Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Luchuan Zeng
- School of Software, Nanchang University, Nanchang, Jiangxi, China
| | - Jie Lei
- School of Software, Nanchang University, Nanchang, Jiangxi, China
| | - Linglin Xia
- School of Software, Nanchang University, Nanchang, Jiangxi, China
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Morphological basis of Parkinson disease-associated cognitive impairment: an update. J Neural Transm (Vienna) 2022; 129:977-999. [PMID: 35726096 DOI: 10.1007/s00702-022-02522-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 05/25/2022] [Indexed: 12/15/2022]
Abstract
Cognitive impairment is one of the most salient non-motor symptoms of Parkinson disease (PD) that poses a significant burden on the patients and carers as well as being a risk factor for early mortality. People with PD show a wide spectrum of cognitive dysfunctions ranging from subjective cognitive decline and mild cognitive impairment (MCI) to frank dementia. The mean frequency of PD with MCI (PD-MCI) is 25.8% and the pooled dementia frequency is 26.3% increasing up to 83% 20 years after diagnosis. A better understanding of the underlying pathological processes will aid in directing disease-specific treatment. Modern neuroimaging studies revealed considerable changes in gray and white matter in PD patients with cognitive impairment, cortical atrophy, hypometabolism, dopamine/cholinergic or other neurotransmitter dysfunction and increased amyloid burden, but multiple mechanism are likely involved. Combined analysis of imaging and fluid markers is the most promising method for identifying PD-MCI and Parkinson disease dementia (PDD). Morphological substrates are a combination of Lewy- and Alzheimer-associated and other concomitant pathologies with aggregation of α-synuclein, amyloid, tau and other pathological proteins in cortical and subcortical regions causing destruction of essential neuronal networks. Significant pathological heterogeneity within PD-MCI reflects deficits in various cognitive domains. This review highlights the essential neuroimaging data and neuropathological changes in PD with cognitive impairment, the amount and topographical distribution of pathological protein aggregates and their pathophysiological relevance. Large-scale clinicopathological correlative studies are warranted to further elucidate the exact neuropathological correlates of cognitive impairment in PD and related synucleinopathies as a basis for early diagnosis and future disease-modifying therapies.
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Bae YJ, Kim JM, Choi BS, Song YS, Nam Y, Cho SJ, Kim JH, Kim SE. MRI Findings in Parkinson’s Disease: Radiologic Assessment of Nigrostriatal Degeneration. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2022; 83:508-526. [PMID: 36238511 PMCID: PMC9514534 DOI: 10.3348/jksr.2022.0044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/06/2022] [Accepted: 05/12/2022] [Indexed: 11/15/2022]
Abstract
파킨슨병은 중뇌 흑질에 위치한 도파민성 신경세포의 퇴행성 소실로 인해 발생하는 이상운동질환이다. 최근 다양한 자기공명영상기법의 발전으로 파킨슨병에서 일어나는 병리생태학적인 변화를 반영하는 여러 영상 소견들이 보고되었다. 여러 연구에서 이러한 영상 소견들은 파킨슨병의 진단 및 비정형 파킨슨증과의 감별 등에 유의미한 도움을 줄 수 있는 것이 밝혀졌다. 본 종설에서는, 파킨슨병에서 일어나는 흑질선조체 변성의 병태생리를 나타낼 수 있는 나이그로좀 영상 및 뉴로멜라닌 영상 등을 포함한 자기공명영상기법들과 각 영상에서 나타나는 소견에 대하여 자세히 다루었다.
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Affiliation(s)
- Yun Jung Bae
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Jong-Min Kim
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Byung Se Choi
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Yoo Sung Song
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Yoonho Nam
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea
| | - Se Jin Cho
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Jae Hyoung Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Sang Eun Kim
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
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