Diagnosis of multiple sclerosis using optical coherence tomography supported by explainable artificial intelligence.

BACKGROUND/OBJECTIVES: Study of retinal structure based on optical coherence tomography (OCT) data can facilitate early diagnosis of relapsing-remitting multiple sclerosis (RRMS). Although artificial intelligence can provide highly reliable diagnoses, the results obtained must be explainable. SUBJECTS/METHODS: The study included 79 recently diagnosed RRMS patients and 69 age matched healthy control subjects. Thickness (Avg) and inter-eye difference (Diff) features are obtained in 4 retinal layers using the posterior pole protocol. Each layer is divided into six analysis zones. The Support Vector Machine plus Recursive Feature Elimination with Leave-One-Out Cross Validation (SVM-RFE-LOOCV) approach is used to find the subset of features that reduces dimensionality and optimises the performance of the classifier. RESULTS: SVM-RFE-LOOCV was used to identify OCT features with greatest capacity for early diagnosis, determining the area of the papillomacular bundle to be the most influential. A correlation was observed between loss of layer thickness and increase in functional disability. There was also greater functional deterioration in patients with greater asymmetry between left and right eyes. The classifier based on the top-ranked features obtained sensitivity = 0.86 and specificity = 0.90. CONCLUSIONS: There was consistency between the features identified as relevant by the SVM-RFE-LOOCV approach and the retinotopic distribution of the retinal nerve fibres and the optic nerve head. This simple method contributes to implementation of an assisted diagnosis system and its accuracy exceeds that achieved with magnetic resonance imaging of the central nervous system, the current gold standard. This paper provides novel insights into RRMS affectation of the neuroretina.

Early diagnosis of multiple sclerosis by OCT analysis using Cohen's d method and a neural network as classifier.

BACKGROUND: The consequences of inflammation, demyelination, axonal degeneration and neuronal loss in the central nervous system, typical of the development of multiple sclerosis (MS), are manifested in thinning of the retina and optic nerve. The purpose of this work is to diagnose early-stage MS patients based on analysis of retinal layer thickness obtained by swept-source optical coherence tomography (SS-OCT). METHOD: OCT (Triton® SS-OCT device -Topcon, Tokyo, Japan-) recordings were obtained from 48 control subjects and 48 recently diagnosed MS patients. The following thicknesses were measured on a 45 × 60 grid: retinal nerve fibre layer (RNFL), ganglion cell layer (GCL+), GCL++, retinal thickness and choroid. Using Cohen's d effect size, it was determined the regions and layers with greatest capacity to discriminate between control subjects and patients. Points exceeding the threshold set were used as inputs for an automatic classifier: support vector machine and feed-forward neural network. RESULTS: In MS at clinical onset the layer with greatest discriminant capacity is GCL++ [AUC = 0.83] which exhibits a horseshoe-like macular topographic distribution. It is followed by retina, GCL+ and RNFL; choroidal thicknesses do not provide discriminatory capacity. Using a neural network as a classifier between controls and MS patients, obtains sensitivity of 0.98 and specificity of 0.98. CONCLUSIONS: This work suggest that OCT may serve as an important complementary role to other clinical tests, particularly regarding neurodegeneration. It is possible to characterise structural alterations in retina and diagnose early-stage MS with high degree of accuracy using OCT and artificial neural networks.

Identification of clusters in multifocal electrophysiology recordings to maximize discriminant capacity (patients vs. control subjects).

PURPOSE: To propose a new method of identifying clusters in multifocal electrophysiology (multifocal electroretinogram: mfERG; multifocal visual-evoked potential: mfVEP) that conserve the maximum capacity to discriminate between patients and control subjects. METHODS: The theoretical framework proposed creates arbitrary N-size clusters of sectors. The capacity to discriminate between patients and control subjects is assessed by analysing the area under the receiver operator characteristic curve (AUC). As proof of concept, the method is validated using mfERG recordings taken from both eyes of control subjects (n = 6) and from patients with multiple sclerosis (n = 15). RESULTS: Considering the amplitude of wave P1 as the analysis parameter, the maximum value of AUC = 0.7042 is obtained with N = 9 sectors. Taking into account the AUC of the amplitudes and latencies of waves N1 and P1, the maximum value of the AUC = 0.6917 with N = 8 clustered sectors. The greatest discriminant capacity is obtained by analysing the latency of wave P1: AUC = 0.8854 with a cluster of N = 12 sectors. CONCLUSION: This paper demonstrates the effectiveness of a method able to determine the arbitrary clustering of multifocal responses that possesses the greatest capacity to discriminate between control subjects and patients when applied to the visual field of mfERG or mfVEP recordings. The method may prove helpful in diagnosing any disease that is identifiable in patients' mfERG or mfVEP recordings and is extensible to other clinical tests, such as optical coherence tomography.

A computer-aided diagnosis of multiple sclerosis based on mfVEP recordings.

INTRODUCTION: The aim of this study is to develop a computer-aided diagnosis system to identify subjects at differing stages of development of multiple sclerosis (MS) using multifocal visual-evoked potentials (mfVEPs). Using an automatic classifier, diagnosis is performed first on the eyes and then on the subjects. PATIENTS: MfVEP signals were obtained from patients with Radiologically Isolated Syndrome (RIS) (n = 30 eyes), patients with Clinically Isolated Syndrome (CIS) (n = 62 eyes), patients with definite MS (n = 56 eyes) and 22 control subjects (n = 44 eyes). The CIS and MS groups were divided into two subgroups: those with eyes affected by optic neuritis (ON) and those without (non-ON). METHODS: For individual eye diagnosis, a feature vector was formed with information about the intensity, latency and singular values of the mfVEP signals. A flat multiclass classifier (FMC) and a hierarchical classifier (HC) were tested and both were implemented using the k-Nearest Neighbour (k-NN) algorithm. The output of the best eye classifier was used to classify the subjects. In the event of divergence, the eye with the best mfVEP recording was selected. RESULTS: In the eye classifier, the HC performed better than the FMC (accuracy = 0.74 and extended Matthew Correlation Coefficient (MCC) = 0.68). In the subject classification, accuracy = 0.95 and MCC = 0.93, confirming that it may be a promising tool for MS diagnosis. CONCLUSION: In addition to amplitude (axonal loss) and latency (demyelination), it has shown that the singular values of the mfVEP signals provide discriminatory information that may be used to identify subjects with differing degrees of the disease.

Emotion processing and psychosocial functioning in euthymic bipolar disorder.

OBJECTIVE: To examine emotion processing in euthymic bipolar patients (EBP) compared to healthy controls. In addition, to determine whether or not there is an association between emotion processing and psychosocial functioning. MATERIAL AND METHODS: A sample of 60 EBP and 60 healthy controls matched for age, gender, education level, and premorbid intelligence were studied. All subjects were assessed using the MATRICS Consensus Cognitive Battery (MCCB) and two additional executive function measures: the Trail Making Test-Part B and the Stroop Test. Emotion processing was examined using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). Psychosocial functioning was assessed using the Functional Assessment Short Test (FAST). RESULTS: Euthymic bipolar patients obtained lower scores than controls in all MSCEIT measures except for the using emotions branch. Likewise, EBP obtained a worse performance than healthy controls in all neurocognitive domains. Correlation between MSCEIT strategic area measures and FAST total score was found (r = -0.311; P < 0.016). Regression analysis showed that residual depressive symptomatology explains a 9.1% of the variance in functional outcome. MSCEIT strategic area score explained an additional 8.6%. Neurocognition did not increase the percentage of the variance explained by emotion processing. CONCLUSIONS: Euthymic bipolar patients exhibit deficits in emotion processing. Emotion processing is associated with social functioning in these patients.

Prepulse inhibition in euthymic bipolar disorder patients in comparison with control subjects.

OBJECTIVE: Deficient prepulse inhibition (PPI) of the startle response, indicating sensorimotor gating deficits, has been reported in schizophrenia and other neuropsychiatric disorders. This study aimed to assess sensorimotor gating deficits in patients with euthymic bipolar. Furthermore, we analysed the relationships between PPI and clinical and cognitive measures. METHOD: Prepulse inhibition was measured in 64 patients with euthymic bipolar and in 64 control subjects matched for age, gender, education level and smoking status. Clinical characteristics and level of functioning were assessed in all participants using Hamilton Depression Rating Scale (HDRS), Young Mania Rating Scale (YMRS) and Functioning Assessment Short Test (FAST). Cognition was evaluated using the Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery (MCCB) and the Stroop test as an additional measure of executive function. RESULTS: Compared with controls, patients with bipolar disorder exhibited PPI deficits at 60- and 120-millisecond prepulse-pulse intervals. Among patients with bipolar disorder, PPI was correlated with the social cognition domain of the MCCB. PPI was not significantly correlated with other clinical, functional and neurocognitive variables in either group. CONCLUSIONS: Our data suggest that PPI deficit is a neurobiological marker in euthymic bipolar disorder, which is associated with social cognition but not with other clinical, functional or cognitive measures.

A signal-to-noise-ratio-based analysis of multifocal visual-evoked potentials in multiple sclerosis risk assessment.

OBJECTIVE: To study the value of using the signal-to-noise ratio (SNR) of multifocal visual-evoked potentials (mfVEPs) in assessment of subjects at risk of developing multiple sclerosis (MS). METHODS: MfVEP signals were obtained from 15 patients with radiologically isolated syndrome (RIS), from 28 patients with clinically isolated syndrome (CIS), from 28 with clinically definite MS and from 24 control subjects. The CIS and MS groups were divided into two subgroups: those with eyes affected by optic neuritis (ON) and those without (non-ON). The mfVEPs' SNR was obtained for both the whole visual field and at various eccentric rings. The area under the curve (AUC) was calculated by comparing the control subjects' mfVEP SNR values with those of the RIS, CIS and MS groups. RESULTS: In whole visual field analysis, risk of developing MS increased as SNR decreased (SNRCONTROL=0.70, SNRRIS=0.62, SNRCIS-nonON=0.54, SNRCIS-ON=0.40, SNRMS-nonON=0.52, SNRMS-ON=0.40). Ring 5 (9.8°-15° eccentricity) was most affected by the SNR decrease, as indicated by its higher AUC values (AUCFULL_EYE=0.81, AUCRING_5=0.89). A significant relationship (Spearman correlation, ρRING_5=0.61) between SNR values and disability severity on the Expanded Disability Status Scale (EDSS) was observed in clinically definite MS patients. CONCLUSION: A new method based on analysis of the SNR of mfVEP signal amplitude improves assessment of patients at risk of developing MS. SIGNIFICANCE: Improved mfVEP assessment of MS-risk patients was achieved by using SNR values at 9.8°-15° eccentricity of the visual field.

Filtering multifocal VEP signals using Prony's method.

BACKGROUND: This paper describes use of Prony's method as a filter applied to multifocal visual-evoked-potential (mfVEP) signals. Prony's method can be viewed as an extension of Fourier analysis that allows a signal to be decomposed into a linear combination of functions with different amplitudes, damping factors, frequencies and phase angles. METHOD: By selecting Prony method parameters, a frequency filter has been developed which improves signal-to-noise ratio (SNR). Three different criteria were applied to data recorded from control subjects to produce three separate datasets: unfiltered raw data, data filtered using the traditional method (fast Fourier transform: FFT), and data filtered using Prony's method. RESULTS: Filtering using Prony's method improved the signals' original SNR by 44.52%, while the FFT filter improved the SNR by 33.56%. The extent to which signal can be separated from noise was analysed using receiver-operating-characteristic (ROC) curves. The area under the curve (AUC) was greater in the signals filtered using Prony's method than in the original signals or in those filtered using the FFT. CONCLUSION: filtering using Prony's method improves the quality of mfVEP signal pre-processing when compared with the original signals, or with those filtered using the FFT.

Filtering mfVEP signals using Wiener filter.

The multifocal visual-evoked-potential (mfVEP) signals are filtered using the Wiener filter combined with a Fast Fourier Transform and their signal-to-noise ratios are compared against those of unfiltered signals (RAW data) and those of signals filtered using the traditional method (FFT data). The Wiener filter improves the original signals' SNR by 37.49%, while the FFT improves the SNR by 20.41%. This gain is achieved by selecting the best channel in each sector of the visual field. In conclusion, filtering using the Wieners method improves the quality of mfVEP signal pre-processing when compared against the original signals, or against filtering using the FFT.

Improved measurement of intersession latency in mfVEPs.

PURPOSE: The purpose of the study is to present a method (Selfcorr) by which to measure intersession latency differences between multifocal VEP (mfVEP) signals. METHODS: The authors compared the intersession latency difference obtained using a correlation method (Selfcorr) against that obtained using a Template method. While the Template method cross-correlates the subject's signals with a reference database, the Selfcorr method cross-correlates traces across subsequent recordings taken from the same subject. RESULTS: The variation in latency between intersession signals was 0.8 ± 13.6 and 0.5 ± 5.0 ms for the Template and Selfcorr methods, respectively, with a coefficient of variability CV_TEMPLATE = 15.83 and CV_SELFCORR = 5.68 (n = 18, p = 0.0002, Wilcoxon). The number of analyzable sectors with the Template and Selfcorr methods was 36.7 ± 8.5 and 45.3 ± 8.7, respectively (p = 0.0001, paired t test, two tailed). CONCLUSIONS: The Selfcorr method produces smaller intersession mfVEP delays and variability over time than the Template method.

Differential relationships between set-shifting abilities and dimensions of insight in schizophrenia.

BACKGROUND: To assess insight in a large sample of patients with schizophrenia and to study its relationship with set shifting as an executive function. METHODS: The insight of a sample of 161 clinically stable, community-dwelling patients with schizophrenia was evaluated by means of the Scale to Assess Unawareness of Mental Disorder (SUMD). Set shifting was measured using the Trail-Making Test time required to complete part B minus the time required to complete part A (TMT B-A). Linear regression analyses were performed to investigate the relationships of TMT B-A with different dimensions of general insight. RESULTS: Regression analyses revealed a significant association between TMT B-A and two of the SUMD general components: 'awareness of mental disorder' and 'awareness of the efficacy of treatment'. The 'awareness of social consequences' component was not significantly associated with set shifting. CONCLUSIONS: Our results show a significant relation between set shifting and insight, but not in the same manner for the different components of the SUMD general score.

Sustained attention as a potential endophenotype for bipolar disorder.

OBJECTIVE: Nowadays, it is accepted that to identify the biological basis of psychiatric illnesses it would be useful to deconstruct them into the most basic manifestations, such as cognitive deficits. The aim of this study was to set attention deficit as a stable vulnerability marker of bipolar disorder. METHOD: Sustained attention was evaluated by the Continuous Performance Test (DS-CPT) in 143 euthymic bipolar patients and 105 controls. To estimate the influence of clinical profile in attention, patients completed a semi-structured interview. RESULTS: Bipolar patients showed a deficit in attention during euthymic periods. This disturbance correlated with years of evolution, age of onset and age of first hospitalisation; and was not influenced by other clinical data. CONCLUSION: Sustained attention may be considered as an endophenotype of the illness.

P50 sensory gating deficit is a common marker of vulnerability to bipolar disorder and schizophrenia.

OBJECTIVE: P50 gating in schizophrenia has contributed much to our understanding of the pathophysiology of the illness. We examined euthymic bipolar patients to determine if they also have a P50 gating deficit. METHOD: P50 gating was measured in 81 euthymic bipolar patients (50 with a lifetime history of psychotic symptoms), 92 stable schizophrenic patients, and 67 control subjects. RESULTS: P50 gating was significantly lower in control subjects than in bipolar patients with a lifetime history of psychosis (P = 0.001) and schizophrenic patients (P = 0.0001). In all patient groups, the percentage of patients with P50 gating was higher than in the control group (chi(2) = 30.596; P < 0.0001). There was no statistically significant correlation between P50 gating and other clinical variables. CONCLUSION: Our data suggest that P50 gating deficit is a neurobiological marker that is present in stable schizophrenic patients and euthymic bipolar patients.

Síndrome de Norrie. Descripción clínica y abordaje terapeútico de un caso

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