At the Heart of Neurological Dimensionality: Cross-Nosological and Multimodal Cardiac Interoceptive Deficits

Abstract

Objective: Neurological nosology, based on categorical systems, has largely ignored dimensional aspects of neurocognitive impairments. Transdiagnostic dimensional approaches of interoception (the sensing of visceral signals) may improve the descriptions of cross-pathological symptoms at behavioral, electrophysiological, and anatomical levels. Alterations of cardiac interoception (encompassing multidimensional variables such as accuracy, learning, sensibility, and awareness) and its neural correlates (electrophysiological markers, imaging-based anatomical and functional connectivity) have been proposed as critical across disparate neurological disorders. However, no study has examined the specific impact of neural (relative to autonomic) disturbances of cardiac interoception or their differential manifestations across neurological conditions.

Methods: Here, we used a computational approach to classify and evaluate which markers of cardiac interoception (behavioral, metacognitive, electrophysiological, volumetric, or functional) offer the best discrimination between neurological conditions and cardiac (hypertensive) disease (model 1), and among neurological conditions (Alzheimer's disease, frontotemporal dementia, multiple sclerosis, and brain stroke; model 2). In total, the study comprised 52 neurological patients (mean [standard deviation] age = 55.1 [17.3] years; 37 women), 25 cardiac patients (age = 66.2 [9.1] years; 13 women), and 72 healthy controls (age = 52.65 [17.1] years; 50 women).

Results: Cardiac interoceptive outcomes successfully classified between neurological and cardiac conditions (model 1: >80% accuracy) but not among neurological conditions (model 2: 53% accuracy). Behavioral cardiac interoceptive alterations, although present in all conditions, were powerful in differentiating between neurological and cardiac diseases. However, among neurological conditions, cardiac interoceptive deficits presented more undifferentiated and unspecific disturbances across dimensions.

Conclusions: Our result suggests a diffuse pattern of interoceptive alterations across neurological conditions, highlighting their potential role as dimensional, transdiagnostic markers.

FIGURE 1

Research framework. A, In the HBD task, subjects were asked to follow their heartbeats by pressing a keyboard key. Four interoceptive accuracy indexes were calculated. B, After each block, the participants rated their confidence about their performance; this score was used to derive the confidence and metacognition indexes. C, HEP is a negativity presented between 200 and 400 milliseconds after the R-peak wave of the heartbeat. Two windows were selected: one between 200 and 300 milliseconds, and the other one between 300 and 400 milliseconds. D, Interoceptive regions of interest were selected from the brain volume analysis for the classification analysis: bilateral middle cingulate cortex (green), anterior cingulate cortex (red), postcentral gyrus (fuchsia), and insula (blue). E, Based on resting-state recordings, we calculated functional connectivity between the same areas considered for structural analysis. F, Classification linear discriminant analyses were performed with one feature selected from each level and dimension previously presented; a feature relevance analysis was then performed to determine the weight each feature had in the classification. HBD = heartbeat detection; HEP = Heart-evoked potential; fMRI = functional magnetic resonance imaging; VBM = Voxel-based morphometry; LDA = Linear discriminant analysis.

FIGURE 2

Main results. I, The average rank of features is shown for each dimension, presenting the first and the last four variables ranked. II1, Model 1: classification between the neurological and the cardiac group. II2, Model 2: classification among neurological conditions. A. Confusion matrix of each analysis. B, Accuracy, specificity, and sensitivity are represented for both analyses. Sphere size is proportional to accuracy value. II3, Feature relevance analysis for each model (A, for the comparison between the neurological and the cardiac group; B, for the comparison among neurological conditions). These figures show the model’s accuracy as each dimension is added following the order of importance of each feature. L = left; R = right; Acc = accuracy; B = bilateral; cond. = condition; AD = Alzheimer’s disease; ST = stroke; MS = multiple sclerosis; FTD = (behavioral variant) frontotemporal dementia; NEU = neurological; CAR = cardiac; OT = other neurological diseases; HDB = heartbeat detection task (accuracy); HEP = heart-evoked potential; MRI = magnetic resonance imaging; fMRI = functional magnetic resonance imaging; ROI = region of interest; W = window.