Amyotrophic lateral sclerosis (ALS) is a heterogeneous neurological disease with limited disease-modifying treatment options and, for many patients, a short survival window. The clinical course varies widely. Limb weakness, bulbar impairment, respiratory decline, fine-motor dysfunction, cognitive change, mood symptoms, and fatigue may each appear at different times and progress at different rates. This variability suggests that motor neuron loss alone may not fully explain the patient-level pattern of symptoms. This article is a narrative hypothesis framework, not a clinical guideline or a validated stratification tool. Established ALS biology, associative genomic findings, preclinical observations, computational predictions, and author-derived hypotheses are therefore separated throughout the article. This review brings together four interlinked studies by the current author as a primary hypothesis-generating corpus, which proposes that synaptic plasticity fragility may initiate a microglial pruning continuum shared by major depressive disorder and ALS, while ALS-specific progression may depend on mitochondrial stress, oxidized nicotinamide adenine dinucleotide (NAD+) compensation failure, and collapse of autophagy under aging-related limits. The model presented here maps symptom domains to vulnerable circuit compartments and separates three broad biological states: compensated plasticity, fragile plasticity, and network collapse. A compact mechanistic formulation is used to describe the balance between pruning pressure, glutamatergic burden, and aging stress on one side, and oxidative phosphorylation capacity, NAD+ reserve, and autophagic clearance on the other. The framework also incorporates opposing phosphoinositide 3-kinase (PI3K)/AKT/mechanistic target of rapamycin (mTOR) and peroxisome proliferator-activated receptor-gamma coactivator-1alpha (PGC-1α) pathway patterns that may distinguish ALS from frontotemporal dementia (FTD) within an aging context. The result is a falsifiable, biomarker-oriented hypothesis model for future studies, not an evidence-based diagnostic or therapeutic algorithm.
