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Objectives Despite increasing evidence demonstrating structural and functional alterations within the central nervous system in diabetic peripheral neuropathy (DPN), the neuroanatomical correlates of Painful- and Painless-DPN have yet to be identified. Focusing on structural magnetic resonance (MR) imaging, the aims of this study were to 1) define the brain morphological alterations in Painful- and Painless-DPN and 2) explore the relationships between brain morphology and clinical/neurophysiological assessments. Research Design and Methods 277 participants with type 1 and 2 diabetes (No-DPN [n=57]; Painless-DPN [n=77]; and Painful-DPN [n=77]) and 66 healthy volunteers (HV) were enrolled. All underwent detailed clinical/neurophysiological assessment and brain MR imaging at 3-Tesla. Painful-DPN participants were subdivided into the irritable nociceptor (IR) and the non-irritable nociceptor (NIR) phenotype using the German Research Network on Neuropathic Painprotocol. Cortical reconstruction and volumetric segmentation were performed with FreeSurfer software and voxel-based morphometry implemented in FSL. Results Both Painful- and Painless-DPN had significant reduction in primary somatosensory and motor cortical thickness compared to HV and No-DPN [P=0.02; F(3,275)=3.36 and P=0.01; F(3,275)=3.80 respectively]. Somatomotor cortical thickness correlated with neurophysiological measures of DPN severity. There was also reduction in ventrobasal thalamic nuclei volume in both Painless- and Painful-DPN. Painful-DPN subjects with the NIR nociceptor phenotype had reduced primary somatosensory, posterior cingulate cortical and thalamic volume compared to the IR nociceptor phenotype. Conclusions In this, the largest neuroimaging study in DPN to date, we have demonstrated significant structural alterations in key somatomotor/nociceptive brain regions specific to Painless-DPN and Painful-DPN including the IR and NIR nociceptor phenotype.

Painful diabetic neuropathy (DN) is a common and distressing complication. New approaches are required to identify novel targets that will serve as a catalyst for future drug discovery programmes. We have demonstrated that responders to neuropathic pain treatment have greater functional connectivity between the insula cortex and the corticolimbic system compared to non-responders. Activity within these networks is mediated by endogenous opioid receptor systems and may hold clues to possible future treatment targets. Methods: 43 painful-DN subjects [responders (VAS Disclosure K.Teh: None. G.P.Sloan: None. I.D.Wilkinson: None. S.Tesfaye: Advisory Panel; Astellas Pharma Inc., Bayer AG, Grünenthal Group, Nevro Corp., Wörwag Pharma GmbH & Co. KG, Speaker's Bureau; Eva Pharma, Pfizer Inc., Viatris Inc. D.Selvarajah: None. Funding National Institute of Health Research EME grant; European Foundation for the Study of Diabetes

Objective: We have previously demonstrated increased thalamic blood volume and preserved HMRS neuronal metabolite ratios in painful diabetic peripheral neuropathy (P-DPN) . We hypothesised that perfusion measures and neuronal function measured by metabolite ratios may be related. However, as brain metabolite ratios may also be affected by mood disorders, common in P-DPN, this may be a significant confounding factor. Methods: 52 type 1 diabetes (T1D) subjects (18 P-DPN, 23 DPN, 13 without neuropathy-DM-NN) and 18 healthy volunteers (HV) were recruited. 1HMRS was performed at 3T (Philips, Netherlands) . Single voxel spectra were obtained from a 2.25cm3 (15x10x15mm) volume of interest within the thalamus, TE=135ms, TR=1600ms, NSA=256 using point resolved (PRESS) technique. Metabolite ratios were calculated for choline, Creatine, and N-Acetyl Aspartate (NAA) . MR-DSC images were obtained at 3T using a T2*-weighted technique (TR/TE=1250/35ms; 72 dynamics) to assess the passage of bolus intravenous gadolinium-chelate through the thalamus. Results: There was significant negative correlation between NAA/Cr (measure of neuronal function) and measures of depression: Hospital Anxiety and Depression Scale R= -0.33 (p=0.01) , Becks Depression Inventory R= -0.25 (p=.048) ; and anxiety: State-Trait Anxiety inventory- State (STAI-S) R= -.37 (p=.002) , STAI- Trait (T) R= -.32 (p=0.01) and Behavioural Inhibition R= -0.25 (p=0.04) . There were no differences in metabolite ratios between groups; and no significant correlations between perfusion measures and metabolite ratios. Conclusion: This is the first study to show thalamic 1HMRS metabolite ratios are correlated with measures of mood disorders in T1D. The high prevalence of mood disorders in P-DPN and DPN have confounded previous 1HMRS studies and may explain conflicting reports in the literature. The link between neuropathy and mood disorders needs further exploration to understand whether both may arise from a common neurobiological pathway. Disclosure M.Greig: None. G.P.Sloan: None. P.Shillo: None. D.Selvarajah: None. I.D.Wilkinson: None. S.Tesfaye: Advisory Panel; Astellas Pharma Inc., Bayer AG, Grünenthal Group, Nevro Corp., Wörwag Pharma GmbH & Co. KG, Speaker's Bureau; Eva Pharma, Pfizer Inc., Viatris Inc. Funding European Foundation for the Study of Diabetes (EFSD)

Aim: Our study predicted treatment responses in patients with painful DPN (diabetic peripheral neuropathy) by developing a deep learning model using resting state functional magnetic resonance imaging (fMRI) neuroimaging datasets. Methods: Forty-three consecutive patients who received intravenous lidocaine treatment for painful DPN were assessed. All subjects (responders n=29 and non-responders n=14) underwent detailed clinical and neurophysiological assessments to phenotype their pain sensory profile. Subjects also underwent brain resting-state fMRI. After pre-processing we performed a group concatenated independent component analysis (ICA) set to 30 components and automatically chose 7 highly correlated (p Results: The deep learning treatment response classification in a ten-fold cross validation experiment using 7 ICA spatial maps has a mean AUC of 0.85 and an F1-Score of 0.90. However, with the extra information of all 30 ICA maps the mean AUROC increased to 0.97 with an F1-Score of 0.95. Using only pre-processed resting-state fMRI data achieved suboptimal F1-Score of 69% and AUC score of 44%. Conclusion: Through the use of our deep learning model, we have demonstrated high classification performance. Our method improves painful DPN treatment efficiency by stratifying patients to receive the correct treatment from the outset. We believe to our knowledge this is the first study utilising deep learning methods to classify treatment response in painful DPN. Disclosure K.Teh: None. I.D.Wilkinson: None. G.P.Sloan: None. S.Tesfaye: Advisory Panel; Astellas Pharma Inc., Bayer AG, Grünenthal Group, Nevro Corp., Wörwag Pharma GmbH & Co. KG, Speaker's Bureau; Eva Pharma, Pfizer Inc., Viatris Inc. D.Selvarajah: None. Funding National Instituteof Health Research Efficacy and Mechanism Evaluation Programme (NIHR 129921) European Foundation for the Study of Diabetes (Microvascular Complications Project Grant) University of Sheffield, Health Education England, Knowledge Exchange

Background Schizophrenia endophenotypes may help elucidate functional effects of genetic risk variants in multiply affected consanguineous families that segregate recessive risk alleles of large effect size. We studied the association between a schizophrenia risk locus involving a 6.1Mb homozygous region on chromosome 13q22–31 in a consanguineous multiplex family and cognitive functioning, haemodynamic response and white matter integrity using neuroimaging. Methods We performed CANTAB neuropsychological testing on four affected family members (all homozygous for the risk locus), ten unaffected family members (seven homozygous and three heterozygous) and ten healthy volunteers, and tested neuronal responses on fMRI during an n-back working memory task, and white matter integrity on diffusion tensor imaging (DTI) on four affected and six unaffected family members (four homozygous and two heterozygous) and three healthy volunteers. For cognitive comparisons we used a linear mixed model (Kruskal–Wallis) test, followed by posthoc Dunn's pairwise tests with a Bonferroni adjustment. For fMRI analysis, we counted voxels exceeding the p < 0.05 corrected threshold. DTI analysis was observational. Results Family members with schizophrenia and unaffected family members homozygous for the risk haplotype showed attention (p < 0.01) and working memory deficits (p < 0.01) compared with healthy controls; a neural activation laterality bias towards the right prefrontal cortex (voxels reaching p < 0.05, corrected) and observed lower fractional anisotropy in the anterior cingulate cortex and left dorsolateral prefrontal cortex. Conclusions In this family, homozygosity at the 13q risk locus was associated with impaired cognition, white matter integrity, and altered laterality of neural activation.

BackgroundSporadic Parkinson’s disease (sPD) is an aetiologically heterogeneous disorder. Identification of distinct pathogenic mechanisms causing sPD will be crucial to develop future “Precision Medicine” approaches. 31P-MRS is a non-invasive tool that can quantify key bionenergetic metabolites in individual patients.ObjectiveTo determine whether 31P-MRS can identify mitochondrial dysfunction in the midbrain/sub- stantia nigra of individual PD patients and correlates with trial-relevant clinical aspects of PD.Methods31P-MRS spectra were obtained from 35 sPD patients and 25 healthy, age-matched controls. Spectra were analysed using the jMRUI software package and AMARES spectral fitting algorithm. Clinical assessment included widely utilised clinical rating scales, genetic analysis and the calculation of predicted risk of rapid disease progression.ResultsThere was a significantly broader variance in 31P-MRS midbrain ATP with 1/3 of all PD patients having ATP levels > 2 standard deviations outside the mean control values (p=0.0030). Higher midbrain phosphocreatine was associated with greater risk of rapid disease progression (p= 0.0384).Conclusions31P-MRS may help to identify a subgroup of sPD with significant mitochondrial dysfunction or at higher risk of more rapid progression and facilitate stratification for future precision medicine neu- roprotective trials. Longitudinal studies are required to characterise if changes 31P-MRS measures mirror clinical progression.