Sensory Profile of Bipolar patients with a Neurodevelopmental Phenotype
Palleau, E.; Salmi, I.; Ahamada, K.; Gilson, M.; Silva, C.; Pergeline, H.; Belzeaux, R.; Deruelle, C.; Lefrere, A.
Show abstract
Background: Bipolar disorder (BD) is increasingly conceptualized as a heterogeneous condition with a neurodevelopmental phenotype (NDP) identifying a subgroup with early neurodevelopmental vulnerability and poorer clinical outcomes. Sensory processing (SP) abnormalities are a core feature of neurodevelopmental disorders but remain poorly characterized in BD and may reflect underlying neurodevelopmental liability. We examined whether NDP load is associated with specific SP alterations in euthymic BD patients and whether NDP-based stratification explains SP variability better than conventional BD subtype (BD 1/2). Methods: We assessed 102 euthymic BD patients and 45 healthy controls (HC) using the Adolescent/Adult Sensory Profile (AASP). NDP load (0-3) was computed from nine clinical variables grouped into neonatal, comorbidity, and neurodevelopmental clusters; a median split defined BD without NDP (BD) and BD with NDP (BD-ND). Associations between NDP load and AASP quadrants were analyzed using Spearman correlations with FDR correction. Group differences (BD, BD-ND, HC) were assessed using Welch ANOVA and post-hoc tests. Nested and multivariable linear regressions examined whether NDP classification explained SP variance beyond BD subtype, adjusting for age, sex, anxiety, and residual mood symptoms. Results: Higher NDP load correlated with greater low registration (rho=0.35, p<0.001, q=0.004), sensory sensitivity (rho=0.30, p=0.001, q=0.004), and sensation avoiding (rho=0.23, p=0.014, q=0.040), but not sensation seeking. BD-ND showed higher low registration, sensory sensitivity, and sensation avoiding than BD and HC (all qs<0.01). NDP classification explained more SP variance than BD subtype; with robust associations after adjustment. Conclusions: Sensory processing alterations in BD are dimensionally associated with neurodevelopmental load and more accurately captured by NDP-based stratification than diagnostic subtype. SP alterations may represent a transdiagnostic marker of neurodevelopmental liability within BD, supporting biologically informed stratification approaches.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Strong Genetic Overlaps Between Dimensional and Categorical Models of Bipolar Disorders in a Family Sample 95%
- Neuroimaging Correlates of Emotional Response-Inhibition Discriminate Between Young Depressed Adults With and Without Sub-threshold Bipolar Symptoms 93%
- White Matter Abnormalities in Bipolar II and Unipolar Depression: Evidence from Fixel-Based Analysis 93%
Similar papers in this journal
Similar papers in this journal
- Longitudinal Stability of Mood-Related Resting-State Networks in Youth with Symptomatic Bipolar-I/II Disorder 94%
- The Influence of Phenotyping Method on Structural Neuroimaging Associations with Depression in UK Biobank 93%
- Blood epigenome-wide association studies of suicide attempt in adults with bipolar disorder 93%
Similar papers in this journal
- Research and Diagnostic Algorithmic Rules (RADAR) for mood disorders, recurrence of illness, suicidal behaviors, and the patient’s lifetime trajectory 94%
- Peripheral Metabolic-Redox Signaling as a Core Mechanism of Major Depressive Disorder: Evidence From Deep Metabolomic Phenotyping 90%
- Lowered oxygen saturation and increased body temperature in acute COVID-19 largely predict chronic fatigue syndrome and affective symptoms due to LONG COVID: a precision nomothetic approach 89%
Similar papers in this journal
- Effects of polygenic risk for suicide attempt and risky behavior on brain structure in young people with familial risk of bipolar disorder 94%
- Independent Inheritance of Cognition and Bipolar Disorder in a Family Sample 94%
- Multi-polygenic scores in psychiatry: from disorder-specific to transdiagnostic perspectives 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.