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Kanthi A, Singh D, Manjunath NK, Nagarathna R. Changes in Electrical Activities of the Brain Associated with Cognitive Functions in Type 2 Diabetes Mellitus: A Systematic Review. Clin EEG Neurosci 2024; 55:130-142. [PMID: 35343277 DOI: 10.1177/15500594221089106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Introduction: Electroencephalogram (EEG) has the potentials to decipher the neural underpinnings of cognitive processes in clinical and healthy populations. Objective: The current systematic review is intended to examine the functional brain changes underlying cognitive dysfunctions in T2DM patients. Methods: The review was conducted on studies published in the PubMed, WebofScience, Cochrane, PsycInfo database till June 2021. The keywords used were electroencephalogram, T2DM, cognitive impairment/dysfunction. We considered studies using resting-state EEG and ERP. The preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines were followed to compile the studies. Results: The search yielded a total of 2384 studies. Finally, 16 independent studies were included. There was a pattern of a shift in EEG power observed from higher to lower frequencies in T2DM patients, though to a lesser degree than Alzheimer's disease patients. P300 latency was increased in T2DM patients mainly over frontal, parietal, and posterior regions. P300 and N100 amplitudes were decreased in T2DM patients than in healthy controls. Conclusion: The results indicate that T2DM has consequences for cognitive functions, and it finds a place in the continuum of healthy cognition to dementia.
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Affiliation(s)
- Amit Kanthi
- Department of Yoga and Life Sciences, Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA), Bangalore, India
| | - Deepeshwar Singh
- Department of Yoga and Life Sciences, Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA), Bangalore, India
| | - N K Manjunath
- Department of Yoga and Life Sciences, Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA), Bangalore, India
| | - Raghuram Nagarathna
- Arogyadhama, Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA), Bangalore, India
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Kuang Y, Wu Z, Xia R, Li X, Liu J, Dai Y, Wang D, Chen S. Phase Lag Index of Resting-State EEG for Identification of Mild Cognitive Impairment Patients with Type 2 Diabetes. Brain Sci 2022; 12:brainsci12101399. [PMID: 36291332 PMCID: PMC9599801 DOI: 10.3390/brainsci12101399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/02/2022] [Accepted: 10/07/2022] [Indexed: 11/30/2022] Open
Abstract
Mild cognitive impairment (MCI) is one of the important comorbidities of type 2 diabetes mellitus (T2DM). It is critical to find appropriate methods for early diagnosis and objective assessment of mild cognitive impairment patients with type 2 diabetes (T2DM-MCI). Our study aimed to investigate potential early alterations in phase lag index (PLI) and determine whether it can distinguish between T2DM-MCI and normal controls with T2DM (T2DM-NC). EEG was recorded in 30 T2DM-MCI patients and 30 T2DM-NC patients. The phase lag index was computed and used in a logistic regression model to discriminate between groups. The correlation between the phase lag index and Montreal Cognitive Assessment (MoCA) score was assessed. The α-band phase lag index was significantly decreased in the T2DM-MCI group compared with the T2DM-NC group and showed a moderate degree of classification accuracy. The MoCA score was positively correlated with the α-band phase lag index (r = 0.4812, moderate association, p = 0.015). This work shows that the functional connectivity analysis of EEG may offer an effective way to track the cortical dysfunction linked to the cognitive deterioration of T2DM patients, and the α-band phase lag index may have a role in guiding the diagnosis of T2DM-MCI.
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Affiliation(s)
- Yuxing Kuang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Ziyi Wu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Rui Xia
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Xingjie Li
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Jun Liu
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Yalan Dai
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Dan Wang
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Shangjie Chen
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
- Correspondence: ; Tel.: +86-0755-27788311
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Ding Y, Chu Y, Liu M, Ling Z, Wang S, Li X, Li Y. Fully automated discrimination of Alzheimer's disease using resting-state electroencephalography signals. Quant Imaging Med Surg 2022; 12:1063-1078. [PMID: 35111605 DOI: 10.21037/qims-21-430] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/24/2021] [Indexed: 12/29/2022]
Abstract
Background The Alzheimer's disease (AD) population increases worldwide, placing a heavy burden on the economy and society. Presently, there is no cure for AD. Developing a convenient method of screening for AD and mild cognitive impairment (MCI) could enable early intervention, thus slowing down the progress of the disease and enabling better overall disease management. Methods In the current study, resting-state electroencephalography (EEG) data were acquired from 113 normal cognition (NC) subjects, 116 amnestic MCI patients, and 72 probable AD patients. After preprocessing by an automatic algorithm, features including spectral power, complexity, and functional connectivity were extracted, and machine-learning classifiers were built to differentiate among the 3 groups. The classification performance was evaluated from multiple perspectives, including accuracy, specificity, sensitivity, area under the curve (AUC) with 95% confidence intervals, and compared to the empirical chance level by permutation tests. Results The analysis of variance results (P<0.05 with false discovery rate correction) confirmed the tendency to slow brain activity, reduced complexity, and connectivity with AD progress. By combining the features, the ability of the machine-learning classifiers, especially the ensemble trees, to differentiate among the 3 groups, was significantly better than that of the empirical chance level of the permutation test. The AUC of the classifier with the best performance was 80.08% for AD vs. NC, 70.82% for AD vs. MCI, and 63.95% for MCI vs. NC. Conclusions The current study presented a fully automatic procedure that could significantly distinguish NC, MCI, and AD subjects via resting-state EEG signals. The study was based on a large data set with evidence-based medical diagnosis and provided further evidence that resting-state EEG data could assist in the discrimination of AD patients.
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Affiliation(s)
- Yue Ding
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,iFLYTEK Research, iFLYTEK CO., LTD., Hefei, China
| | - Yinxue Chu
- iFLYTEK Research, iFLYTEK CO., LTD., Hefei, China
| | - Meng Liu
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Zhenhua Ling
- National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, China
| | - Shijin Wang
- iFLYTEK Research, iFLYTEK CO., LTD., Hefei, China.,State Key Laboratory of Cognitive Intelligence, Hefei, China
| | - Xin Li
- iFLYTEK Research, iFLYTEK CO., LTD., Hefei, China.,National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, China
| | - Yunxia Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
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Benwell CSY, Davila-Pérez P, Fried PJ, Jones RN, Travison TG, Santarnecchi E, Pascual-Leone A, Shafi MM. EEG spectral power abnormalities and their relationship with cognitive dysfunction in patients with Alzheimer's disease and type 2 diabetes. Neurobiol Aging 2020; 85:83-95. [PMID: 31727363 PMCID: PMC6942171 DOI: 10.1016/j.neurobiolaging.2019.10.004] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 09/30/2019] [Accepted: 10/07/2019] [Indexed: 12/13/2022]
Abstract
Rhythmic neural activity has been proposed to play a fundamental role in cognition. Both healthy and pathological aging are characterized by frequency-specific changes in oscillatory activity. However, the cognitive relevance of these changes across the spectrum from normal to pathological aging remains unknown. We examined electroencephalography (EEG) correlates of cognitive function in healthy aging and 2 of the most prominent and debilitating age-related disorders: type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD). Relative to healthy controls (HC), patients with AD were impaired on nearly every cognitive measure, whereas patients with T2DM performed worse mainly on learning and memory tests. A continuum of alterations in resting-state EEG was associated with pathological aging, generally characterized by reduced alpha (α) and beta (β) power (AD < T2DM < HC) and increased delta (δ) and theta (θ) power (AD > T2DM > HC), with some variations across different brain regions. There were also reductions in the frequency and power density of the posterior dominant rhythm in AD. The ratio of (α + β)/(δ + θ) was specifically associated with cognitive function in a domain- and diagnosis-specific manner. The results thus captured both similarities and differences in the pathophysiology of cerebral oscillations in T2DM and AD. Overall, pathological brain aging is marked by a shift in oscillatory power from higher to lower frequencies, which can be captured by a single cognitively relevant measure of the ratio of (α + β) over (δ + θ) power.
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Affiliation(s)
- Christopher S Y Benwell
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Psychology, School of Social Sciences, University of Dundee, Dundee, UK.
| | - Paula Davila-Pérez
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Neuroscience and Motor Control Group (NEUROcom), Institute for Biomedical Research (INIBIC), Universidade da Coruña, A Coruña, Spain
| | - Peter J Fried
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Richard N Jones
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University, Butler Hospital, Providence, RI, USA
| | - Thomas G Travison
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Boston, MA, USA
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Alvaro Pascual-Leone
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Boston, MA, USA; Institut Guttman, Universitat Autonoma de Barcelona, Badalona, Barcelona, Spain; Center for Memory Health, Hebrew Senior Life, Boston, MA, USA
| | - Mouhsin M Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Comprehensive Epilepsy Center, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.
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Kodama K, Takamoto K, Nishimaru H, Matsumoto J, Takamura Y, Sakai S, Ono T, Nishijo H. Analgesic Effects of Compression at Trigger Points Are Associated With Reduction of Frontal Polar Cortical Activity as Well as Functional Connectivity Between the Frontal Polar Area and Insula in Patients With Chronic Low Back Pain: A Randomized Trial. Front Syst Neurosci 2019; 13:68. [PMID: 31798422 PMCID: PMC6863771 DOI: 10.3389/fnsys.2019.00068] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 10/28/2019] [Indexed: 12/20/2022] Open
Abstract
Background Compression of myofascial trigger points (MTrPs) in muscles is reported to reduce chronic musculoskeletal pain. Although the prefrontal cortex (PFC) is implicated in development of chronic pain, the mechanisms of how MTrP compression at low back regions affects PFC activity remain under debate. In this study, we investigated effects of MTrP compression on brain hemodynamics and EEG oscillation in subjects with chronic low back pain. Methods The study was a prospective, randomized, parallel-group trial and an observer and subject-blinded clinical trial. Thirty-two subjects with chronic low back pain were divided into two groups: subjects with compression at MTrPs (n = 16) or those with non-MTrPs (n = 16). Compression at MTrP or non-MTrP for 30 s was applied five times, and hemodynamic activity (near-infrared spectroscopy; NIRS) and EEGs were simultaneously recorded during the experiment. Results The results indicated that compression at MTrPs significantly (1) reduced subjective pain (P < 0.05) and increased the pressure pain threshold (P < 0.05), (2) decreased the NIRS hemodynamic activity in the frontal polar area (pPFC) (P < 0.05), and (3) increased the current source density (CSD) of EEG theta oscillation in the anterior part of the PFC (P < 0.05). CSD of EEG theta oscillation was negatively correlated with NIRS hemodynamic activity in the pPFC (P < 0.05). Furthermore, functional connectivity in theta bands between the medial pPFC and insula cortex was significantly decreased in the MTrP group (P < 0.05). The functional connectivity between those regions was positively correlated with subjective low back pain (P < 0.05). Discussion The results suggest that MTrP compression at the lumbar muscle modulates pPFC activity and functional connectivity between the pPFC and insula, which may relieve chronic musculoskeletal pain. Trial registration This trial was registered at University Hospital Medical Information Network Clinical Trials Registry (UMIN000033913) on 27 August 2018, at https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000038660.
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Affiliation(s)
- Kanae Kodama
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Kouichi Takamoto
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, Japan.,Department of Sports and Health Sciences, Faculty of Human Sciences, University of East Asia, Shimonoseki, Japan
| | - Hiroshi Nishimaru
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Jumpei Matsumoto
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Yusaku Takamura
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Shigekazu Sakai
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Taketoshi Ono
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, Japan
| | - Hisao Nishijo
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama, Japan
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Is resting state frontal alpha connectivity asymmetry a useful index to assess depressive symptoms? A preliminary investigation in a sample of university students. J Affect Disord 2019; 257:152-159. [PMID: 31301617 DOI: 10.1016/j.jad.2019.07.034] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 06/13/2019] [Accepted: 07/04/2019] [Indexed: 01/07/2023]
Abstract
BACKGROUND Frontal alpha asymmetry (FAA) has been widely investigated in depressive disorders (DDs) with contradictory and not conclusive results. The main aim of the current study was to explore the association between a new neurophysiological index, the so-called frontal alpha connectivity asymmetry index (FACA-I), and depressive symptoms. METHODS One hundred and thirteen participants (45 men and 68 women, mean age: 22.83 ± 2.26 years) were enrolled. Electroencephalographic (EEG) recordings were performed during 5 min of resting state (RS). FACA-I was computed by subtracting connectivity at left frontal regions from right frontal regions and dividing by their sum. RS FAA were also computed and compared to the FACA-I in all analyses. RESULTS After controlling for the presence of potential confounding variables (i.e., sex, age and anxiety symptoms), only FACA-I scores between medial prefrontal cortex and subgenual anterior cingulate cortex were negatively associated with both somatic and cognitive/affective depressive symptoms and were lower in individuals with significant level of depressive symptoms. LIMITATIONS We focused on a sample of university students with no formal diagnosis of depression and we did not assess FAA and FACA-I during cognitive and/or emotional tasks, which make our interpretation specific to the RS condition. CONCLUSIONS Taken together our data suggest that alpha connectivity asymmetry between medial prefrontal cortex and subgenual anterior cingulate cortex may be a useful neurophysiological index in the assessment of depressive symptoms.
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Lee HY, Jung KI, Yoo WK, Ohn SH. Global Synchronization Index as an Indicator for Tracking Cognitive Function Changes in a Traumatic Brain Injury Patient: A Case Report. Ann Rehabil Med 2019; 43:106-110. [PMID: 30852877 PMCID: PMC6409661 DOI: 10.5535/arm.2019.43.1.106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 06/18/2018] [Indexed: 11/20/2022] Open
Abstract
Traumatic brain injury is a main cause of long-term neurological disability, and many patients suffer from cognitive impairment for a lengthy period. Cognitive impairment is a fatal malady to that limits active rehabilitation, and functional recovery in patients with traumatic brain injury. In severe cases, it is impossible to assess cognitive function precisely, and severe cognitive impairment makes it difficult to establish a rehabilitation plan, as well as evaluate the course of rehabilitation. Evaluation of cognitive function is essential for establishing a rehabilitation plan, as well as evaluating the course of rehabilitation. We report a case of the analysis of electroencephalography with global synchronization index and low-resolution brain electromagnetic tomography applied, for evaluation of cognitive function that was difficult with conventional tests, due to severe cognitive impairment in a 77-year-old male patient that experienced traumatic brain injury.
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Affiliation(s)
- Ho Young Lee
- Department of Physical Medicine and Rehabilitation, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
| | - Kwang-Ik Jung
- Department of Physical Medicine and Rehabilitation, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
| | - Woo-Kyoung Yoo
- Department of Physical Medicine and Rehabilitation, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
| | - Suk Hoon Ohn
- Department of Physical Medicine and Rehabilitation, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
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Cui D, Qi S, Gu G, Li X, Li Z, Wang L, Yin S. Magnitude Squared Coherence Method based on Weighted Canonical Correlation Analysis for EEG Synchronization Analysis in Amnesic Mild Cognitive Impairment of Diabetes Mellitus. IEEE Trans Neural Syst Rehabil Eng 2018; 26:1908-1917. [DOI: 10.1109/tnsre.2018.2862396] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Drewes AM, Søfteland E, Dimcevski G, Farmer AD, Brock C, Frøkjær JB, Krogh K, Drewes AM. Brain changes in diabetes mellitus patients with gastrointestinal symptoms. World J Diabetes 2016; 7:14-26. [PMID: 26839652 PMCID: PMC4724575 DOI: 10.4239/wjd.v7.i2.14] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Revised: 09/14/2015] [Accepted: 10/27/2015] [Indexed: 02/05/2023] Open
Abstract
Diabetes mellitus is a common disease and its prevalence is increasing worldwide. In various studies up to 30%-70% of patients present dysfunction and complications related to the gut. To date several clinical studies have demonstrated that autonomic nervous system neuropathy and generalized neuropathy of the central nervous system (CNS) may play a major role. This systematic review provides an overview of the neurodegenerative changes that occur as a consequence of diabetes with a focus on the CNS changes and gastrointestinal (GI) dysfunction. Animal models where diabetes was induced experimentally support that the disease induces changes in CNS. Recent investigations with electroencephalography and functional brain imaging in patients with diabetes confirm these structural and functional brain changes. Encephalographic studies demonstrated that altered insular processing of sensory stimuli seems to be a key player in symptom generation. In fact one study indicated that the more GI symptoms the patients experienced, the deeper the insular electrical source was located. The electroencephalography was often used in combination with quantitative sensory testing mainly showing hyposensitivity to stimulation of GI organs. Imaging studies on patients with diabetes and GI symptoms mainly showed microstructural changes, especially in brain areas involved in visceral sensory processing. As the electrophysiological and imaging changes were associated with GI and autonomic symptoms they may represent a future therapeutic target for treating diabetics either pharmacologically or with neuromodulation.
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