Somatosensory network functional connectivity differentiates clinical pain phenotypes in diabetic neuropathy

The Role of the Insula in Chronic Pain and Associated Structural Changes: An Integrative Review

Chronic pain affects a substantial portion of the global population, significantly impacting quality of life and well-being. This condition involves complex mechanisms, including dysfunction of the autonomic nervous system, which plays a crucial role in pain perception. The insula, a key brain region involved in pain processing, plays a critical role in pain perception and modulation. Lesions in the insula can result in pain asymbolia, where pain perception remains intact but emotional responses are inappropriate. The insula is anatomically and functionally divided into anterior and posterior regions, with the posterior insula processing nociceptive input based on intensity and location before relaying it to the anterior insula for emotional mediation. Understanding the insula's intricate role in pain processing is crucial, as it is involved in encoding prediction errors and mediating emotional dimensions of pain perception. The focus of this review was on synthesizing existing literature on the role of the insula in chronic pain and associated structural changes. The goal was to integrate findings from various sources to provide a comprehensive overview of the topic. The search strategy included a combination of Medical Subject Headings (MeSH) and relevant keywords related to insula and chronic pain. The following databases were surveyed: PubMed, Embase, Scopus, and Web of Science. We identified a total of 2515 articles, and after following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline based on eligibility criteria, 46 articles were used to synthesize this review. Our study highlights the pivotal role of the insula in chronic pain processing and associated structural changes, integrating findings from diverse studies and neuroimaging investigations. Beyond mere pain sensation, the insula contributes to emotional awareness, attention, and salience detection within the pain network. Various chronic pain conditions reveal alterations in insular activity and connectivity, accompanied by changes in gray matter volume and neurochemical profiles. Interventions targeting the insula show promise in alleviating chronic pain symptoms. However, further research is needed to understand underlying mechanisms, which can aid in developing more effective therapeutic interventions for pain.

New perspectives in diabetic neuropathy

Diabetes prevalence continues to climb with the aging population. Type 2 diabetes (T2D), which constitutes most cases, is metabolically acquired. Diabetic peripheral neuropathy (DPN), the most common microvascular complication, is length-dependent damage to peripheral nerves. DPN pathogenesis is complex, but, at its core, it can be viewed as a state of impaired metabolism and bioenergetics failure operating against the backdrop of long peripheral nerve axons supported by glia. This unique peripheral nerve anatomy and the injury consequent to T2D underpins the distal-to-proximal symptomatology of DPN. Earlier work focused on the impact of hyperglycemia on nerve damage and bioenergetics failure, but recent evidence additionally implicates contributions from obesity and dyslipidemia. This review will cover peripheral nerve anatomy, bioenergetics, and glia-axon interactions, building the framework for understanding how hyperglycemia and dyslipidemia induce bioenergetics failure in DPN. DPN and painful DPN still lack disease-modifying therapies, and research on novel mechanism-based approaches is also covered.

Somatosensory Auras in Epilepsy: A Narrative Review of the Literature

An aura is a subjective experience felt in the initial phase of a seizure. Studying auras is relevant as they can be warning signs for people with epilepsy. The incidence of aura tends to be underestimated due to misdiagnosis or underrecognition by patients unless it progresses to motor features. Also, auras are associated with seizure remission after epilepsy surgery and are an important prognostic factor, guiding the resection site and improving surgical outcomes. Somatosensory auras (SSAs) are characterized by abnormal sensations on one or more body parts that may spread to other parts following a somatotopic pattern. The occurrence of SSAs among individuals with epilepsy can range from 1.42% to 80%. The upper extremities are more commonly affected in SSAs, followed by the lower extremities and the face. The most common type of somatosensory aura is paresthetic, followed by painful and thermal auras. In the primary somatosensory auras, sensations occur more commonly contralaterally, while the secondary somatosensory auras can be ipsilateral or bilateral. Despite the high localizing features of somatosensory areas, cortical stimulation studies have shown overlapping sensations originating in the insula and the supplementary sensorimotor area.

Peripheral Neuropathy in Diabetes: What Can MRI Do?

Diabetes peripheral neuropathy (DPN) is commonly asymptomatic in the early stage. However, once symptoms and obvious defects appear, recovery is not possible. Diagnosis of neuropathy is based on physical examinations, questionnaires, nerve conduction studies, skin biopsies, and so on. However, the diagnosis of DPN is still challenging, and early diagnosis and immediate intervention are very important for prevention of the development and progression of diabetic neuropathy. The advantages of MRI in the diagnosis of DPN are obvious: the peripheral nerve imaging is clear, the lesions can be found intuitively, and the quantitative evaluation of the lesions is the basis for the diagnosis, classification, and follow-up of DPN. With the development of magnetic resonance technology, more and more studies have been conducted on detection of DPN. This article reviews the research field of MRI in DPN.

Deep Learning Classification of Treatment Response in Diabetic Painful Neuropathy: A Combined Machine Learning and Magnetic Resonance Neuroimaging Methodological Study

Functional magnetic resonance imaging (fMRI) has been shown successfully to assess and stratify patients with painful diabetic peripheral neuropathy (pDPN). This supports the idea of using neuroimaging as a mechanism-based technique to individualise therapy for patients with painful DPN. The aim of this study was to use deep learning to predict treatment response in patients with pDPN using resting state functional imaging (rs-fMRI). We divided 43 painful pDPN patients into responders and non-responders to lidocaine treatment (responders n = 29 and non-responders n = 14). We used rs-fMRI to extract functional connectivity features, using group independent component analysis (gICA), and performed automated treatment response deep learning classification with three-dimensional convolutional neural networks (3D-CNN). Using gICA we achieved an area under the receiver operating characteristic curve (AUC) of 96.60% and F1-Score of 95% in a ten-fold cross validation (CV) experiment using our described 3D-CNN algorithm. To our knowledge, this is the first study utilising deep learning methods to classify treatment response in pDPN.

Alterations in Functional Connectivity of Thalamus and Primary Somatosensory Cortex in Painful and Painless Diabetic Peripheral Neuropathy

OBJECTIVE

In this study we aimed to investigate the functional connectivity of brain regions involved in sensory processing in diabetes with and without painful and painless diabetic peripheral neuropathy (DPN) and the association with peripheral nerve function and pain intensity.

RESEARCH DESIGN AND METHODS

In this cross-sectional study we used resting-state functional MRI (fMRI) to investigate functional brain connectivity of 19 individuals with type 1 diabetes and painful DPN, 19 with type 1 diabetes and painless DPN, 18 with type 1 diabetes without DPN, and 20 healthy control subjects. Seed-based connectivity analyses were performed for thalamus, postcentral gyrus, and insula, and the connectivity z scores were correlated with peripheral nerve function measurements and pain scores.

RESULTS

Overall, compared with those with painful DPN and healthy control subjects, subjects with type 1 diabetes without DPN showed hyperconnectivity between thalamus and motor areas and between postcentral gyrus and motor areas (all P ≤ 0.029). Poorer peripheral nerve functions and higher pain scores were associated with lower connectivity of the thalamus and postcentral gyrus (all P ≤ 0.043). No connectivity differences were found in insula (all P ≥ 0.071).

CONCLUSIONS

Higher functional connectivity of thalamus and postcentral gyrus appeared only in diabetes without neuropathic complications. Thalamic/postcentral gyral connectivity measures demonstrated an association with peripheral nerve functions. Based on thalamic connectivity, it was possible to group the phenotypes of type 1 diabetes with painful/painless DPN and type 1 diabetes without DPN. The results of the current study support that fMRI can be used for phenotyping, and with validation, it may contribute to early detection and prevention of neuropathic complications.

Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.

Towards prevention of diabetic peripheral neuropathy: clinical presentation, pathogenesis, and new treatments

Diabetic peripheral neuropathy (DPN) occurs in up to half of individuals with type 1 or type 2 diabetes. DPN results from the distal-to-proximal loss of peripheral nerve function, leading to physical disability and sometimes pain, with the consequent lowering of quality of life. Early diagnosis improves clinical outcomes, but many patients still develop neuropathy. Hyperglycaemia is a risk factor and glycaemic control prevents DPN development in type 1 diabetes. However, glycaemic control has modest or no benefit in individuals with type 2 diabetes, probably because they usually have comorbidities. Among them, the metabolic syndrome is a major risk factor for DPN. The pathophysiology of DPN is complex, but mechanisms converge on a unifying theme of bioenergetic failure in the peripheral nerves due to their unique anatomy. Current clinical management focuses on controlling diabetes, the metabolic syndrome, and pain, but remains suboptimal for most patients. Thus, research is ongoing to improve early diagnosis and prognosis, to identify molecular mechanisms that could lead to therapeutic targets, and to investigate lifestyle interventions to improve clinical outcomes.

Toward Composite Pain Biomarkers of Neuropathic Pain—Focus on Peripheral Neuropathic Pain

Chronic pain affects ~10–20% of the U.S. population with an estimated annual cost of $600 billion, the most significant economic cost of any disease to-date. Neuropathic pain is a type of chronic pain that is particularly difficult to manage and leads to significant disability and poor quality of life. Pain biomarkers offer the possibility to develop objective pain-related indicators that may help diagnose, treat, and improve the understanding of neuropathic pain pathophysiology. We review neuropathic pain mechanisms related to opiates, inflammation, and endocannabinoids with the objective of identifying composite biomarkers of neuropathic pain. In the literature, pain biomarkers typically are divided into physiological non-imaging pain biomarkers and brain imaging pain biomarkers. We review both types of biomarker types with the goal of identifying composite pain biomarkers that may improve recognition and treatment of neuropathic pain.

Early Detection of Diabetic Peripheral Neuropathy by fMRI: An Evidence-Based Review

With the significant rise in the prevalence of diabetes worldwide, diabetic peripheral neuropathy (DPN) remains the most common complication among type 1 and 2 diabetics. The adverse sequelae of DPN, which include neuropathic pain, diabetic foot ulcers and lower-limb amputations, significantly impact quality of life and are major contributors to the biopsychosocial and economic burden of diabetes at the individual, societal and health system levels. Because DPN is often diagnosed in the late stages of disease progression by electromyography (EMG), and neuropathic pain as a result of DPN is difficult to treat, the need for earlier detection is crucial to better ascertain and manage the condition. Among the various modalities available to aid in the early detection of DPN, functional magnetic resonance imaging (fMRI) has emerged as a practical tool in DPN imaging due to its noninvasive radiation-free nature and its ability to relate real-time functional changes reflecting the local oxygen consumption of regions of the CNS due to external stimuli. This review aims to summarize the current body of knowledge regarding the utility of fMRI in detecting DPN by observing central nervous system (CNS) activity changes among individuals with DPN when compared to controls. The evidence to date points toward a tendency for increased activity in various central neuroanatomical structures that can be detected by fMRI and positively correlates with diabetic neuropathic pain.

Impaired brain network architecture as neuroimaging evidence of pain in diabetic neuropathy

AIMS

To investigate alterations in structural brain networks due to chronic diabetic neuropathic pain.

METHODS

The current study recruited 24 patients with painful diabetic neuropathy (PDN) to investigate the influences of chronic pain on the brain. Thirteen patients with painless diabetic neuropathy (PLDN) and 24 healthy adults were recruited as disease and healthy controls. White matter connectivity of the brain networks constructed by diffusion tractography was compared across groups using the Network-based statistic (NBS) method. Graph theoretical analysis was further applied to assess topological changes of the brain networks.

RESULTS

The PDN patients had a significant reduction in white matter connectivity compared with PLDN and controls in the limbic and temporal regions, particularly the insula, hippocampus and parahippocampus, the amygdala, and the middle temporal gyrus. The PDN patients also exhibited an altered topology of the brain networks with reduced global efficiency and betweenness centrality.

CONCLUSION

The current findings indicate that topological alterations of brain networks may serve as a biomarker for pain-induced maladaptive reorganization of the brain in PDN. Given the high prevalence of diabetes worldwide, novel insights from network sciences to investigate the central mechanisms of diabetic neuropathic pain are warranted.