Gradient mapping embeds cortical regions based on similarity of their functional connectivity profiles, yielding low-dimensional axes. The principal gradient commonly spans unimodal sensory-motor cortex to transmodal association cortex, with DMN regions near the transmodal apex [4][5][17]-[24]. This organization is relevant to preclinical AD because Aβ burden and network vulnerability concentrate in association cortex and DMN-centered hubs [24].

For preclinical screening/prognosis, gradient analyses often reduce complexity into interpretable features:

1) Gradient range/distribution (e.g., “compression”),

2) Network centroid shifts (DMN/control vs sensory),

3) Dispersion in gradient space,

4) Template similarity (“fit”) to canonical hierarchy [6].

Operational definitions (common practice). In practice, gradients are typically estimated by embedding a parcel-wise functional connectivity similarity matrix into a low-dimensional space and aligning individual solutions to a reference template to enable comparison [6]. “Gradient compression” generally refers to reduced dynamic range or variance of gradient coordinates (often on the principal unimodal-to-transmodal axis), consistent with weaker separation between unimodal and transmodal systems. “Dispersion” summarizes the spread of regions or networks in gradient space (e.g., within-network dispersion or between-network distances), indexing how differentiated systems are along hierarchy axes. “Template similarity/fit” quantifies how closely an individual’s gradient topography matches a canonical template (e.g., spatial correlation or similarity of network ordering along the gradient), with lower fit indicating altered hierarchical organization.

These summaries can be integrated into longitudinal models without the dimensionality burden of edge-wise connectomes.

Gradient features inherit rs-fMRI vulnerabilities: motion and physiology can distort connectivity structure [25][26]; reliability of FC-derived measures is variable [27]. Multi-site work benefits from harmonization strategies and rigorous QC reporting [28][29].

Common targets include CU Aβ+ cohorts (CSF/PET/plasma-defined), SCD cohorts, and genetically enriched groups. Prognostic heterogeneity is expected and should be modeled rather than treated as noise.

For clinical relevance, studies should prioritize:

1) Cognitive decline slope (trajectory over repeated assessments),

2) Conversion to MCI (time-to-event). Diagnostic/endpoint frameworks are often based on widely used criteria and staging tools [30]-[33].

Hierarchy and network-organization differences are reported in symptomatic AD and MCI, motivating early-stage work and providing face validity for hierarchy-based markers [34].

Biomarker-anchored preclinical studies have reported gradient properties related to CSF biomarker profiles and memory performance in asymptomatic at-risk cohorts, supporting biomarker sensitivity of hierarchy metrics in the absence of overt impairment [35].

A key CU cohort study (ALFA+) combined CSF-defined Aβ status, whole-cortex gradients, and telephone-recorded spontaneous speech. CU Aβ+ participants showed widespread gradient alterations and a “compressed” gradient distribution, while a higher-order hierarchy metric related specifically to speech timing measures rather than pitch-based measures [36]. This triangulates Aβ status → hierarchy reconfiguration → scalable timing behavior, even when conventional neuropsychological separation is small.

Genetic-risk-only cohorts (e.g., family history or APOE enrichment without biomarker confirmation) can show gradient differences consistent with vulnerability, but these effects cannot be attributed to AD pathology per se without Aβ/tau characterization. Such evidence is therefore best used to motivate biomarker-anchored replication and to test whether gradients capture risk-related variability that precedes, and potentially interacts with, emerging AT(N) positivity [35].

Speech integrates lexical access, semantic selection, planning, working memory, and executive control. Crucially, it can be collected remotely, frequently, and processed automatically, enabling within-person trajectories—an advantage for forecasting slope and conversion [7]-[16].

For preclinical deployment, the most robust and scalable features are often transcription-free:

1) speaking time, articulation rate, speech rate,

2) silent pause frequency/duration and pause distribution,

3) disfluency timing patterns (filled pauses, repairs) where reliably detected.

This strategy reduces dependency on ASR accuracy and linguistic normalization.

Minimal deployable remote task set.

To translate timing-first features into scalable remote screening and longitudinal follow-up, a minimal, deployable task set should elicit timing and pausing under complementary cognitive demands.

A practical minimal battery can include: 1) a picture description task to elicit structured discourse under planning demands and enable stable timing/pause quantification in widely used benchmarking contexts [13][14]; 2) a brief autobiographical prompt to increase episodic retrieval and ecological validity, where pausing is mechanistically interpretable as production and retrieval load [37]; and 3) a short verbal fluency task (semantic or phonemic) to stress lexical retrieval and executive control, which is expected to manifest primarily as slowed output and increased pausing under retrieval pressure. Together, these tasks are feasible for phone/smartphone deployment and are compatible with repeated, transcription-free acoustic timing extraction in remote protocols [38].

A long psycholinguistic tradition supports the interpretability of pause/disfluency as markers of planning, lexical retrieval difficulty, and cognitive load:

1) Pause length relates to predictability and production demands [39]-[41].

2) Reviews and models emphasize pauses as windows into formulation processes [42][43].

3) Filled pauses (“uh/um”) have communicative and processing consequences, not only “noise” [44]-[49].

4) Disfluencies can modulate listener expectations and comprehension online [46][48].

These foundations justify why subtle preclinical changes might appear first in timing/pause structure, especially under increased planning or retrieval demands.

Pause-focused AD/MCI research has moved beyond mean pause duration toward pause distributions and multimodal pause characterization:

1) Pauses during autobiographical discourse reflect episodic memory processes in early AD [37].

2) Pause distribution analysis has been proposed as an early marker and tested in connected speech contexts [50].

3) Acoustic pause extraction and encoding strategies enable transcription-free AD detection pipelines [51][52].

4) Benchmarking and reviews reinforce the prominence of timing/pause features across datasets and languages [9]-[13].

Remote speech collection (phone/smartphone/web) introduces device, codec, and environment variability, requiring explicit QC and reporting standards. The field is increasingly supported by:

1) Protocols and cohorts designed for remote or scalable speech capture [38].

2) Benchmark shared tasks/datasets that enable comparability and robustness testing (ADReSS/ADReSSo) [13][14].

3) Open repositories and standardized discourse protocols (DementiaBank/TalkBank ecosystem), which facilitate reproducible feature extraction and cross-study comparison [53]-[59].

Key resources include:

1) TalkBank as an infrastructure for multimedia language data and tools [53].

2) AphasiaBank methods and resources that strongly influenced standardized discourse elicitation/analysis pipelines [57][58].

3) DementiaBank standardized protocols and illustrative analyses for dementia/MCI discourse sampling [56].

4) ADReSS/ADReSSo balanced benchmark datasets for dementia detection and related prediction tasks [13][14].

Recommended: linear mixed-effects (or Bayesian hierarchical) models to estimate individual trajectories and test whether baseline gradients and speech features add incremental predictive value beyond demographics/APOE/baseline cognition [25]-[29][60]-[73]. Speech’s feasibility for high-frequency sampling is a practical advantage for slope estimation.

Recommended: survival analysis (Cox or flexible parametric models) with careful reporting of discrimination and calibration over clinically relevant horizons [73]. With repeated speech, joint models can treat speech features as time-varying predictors.

Prediction work should report calibration and clinical utility (e.g., decision curve analysis), and follow modern reporting and bias-assessment guidance [60]-[64]. Avoiding leakage and optimistic bias is essential, especially with small datasets and extensive feature engineering [69][70].

Hearing loss is common in older adults and is associated with dementia risk; it can also directly influence speech timing via perceptual effort and social engagement pathways. Speech-biomarker studies should measure or adjust for hearing status when feasible [74]-[77]. Depression/anxiety can affect rate and pausing and should be assessed as covariates.

Remote pipelines should log device/codec/sample rate and include QC metrics (clipping, SNR proxies, speech/non-speech ratios). Timing-first features are often more robust than fine-grained voice quality metrics under compression and heterogeneous microphones.

Many lexical and syntactic measures scale with output quantity and task structure; models should control for speech quantity and include task indicators when multiple elicitation paradigms are used.