Hippocampal Atrophy Rate

Fit all supplied longitudinal bilateral hippocampal volumes in log and linear space; signed annual log change and signed volume loss preserve growth rather than forcing atrophy.

Description

Repeated bilateral hippocampal volumes can be summarized by linear loss or by a slope in log volume.

Repeated bilateral hippocampal volumes can be summarized by linear loss or by a slope in log volume. These quantify change on different scales.

The tool returns bilateralVolumesMm3 (bilateral volumes mm3); annualizedLogVolumeChangePercent (annualized log volume change percent); linearAnnualLossMm3 (linear annual loss mm3). Input measurements and model parameters are supplied by the caller; the tool computes the stated summary rather than acquiring those measurements.

When to use Hippocampal Atrophy Rate

Use Hippocampal Atrophy Rate when you already have leftVolumesMm3, rightVolumesMm3, elapsedYears and want bilateralVolumesMm3, annualizedLogVolumeChangePercent, linearAnnualLossMm3. This is useful for checking a supplied calculation, documenting a measurement convention, or comparing observations prepared on the same basis.

How to use Hippocampal Atrophy Rate and understand its results

Enter leftVolumesMm3, rightVolumesMm3, elapsedYears using the field table below. Match the units, ordering and data boundaries described in the calculation.
Run the tool after checking the stated bounds and matching paired observations or categories. A malformed request gives a tool error rather than a usable measurement.
Read bilateralVolumesMm3, annualizedLogVolumeChangePercent, linearAnnualLossMm3. Use the calculation and interpretation below to distinguish amounts, ratios, signs and decision flags where applicable.
Review the supplied parameters and assumptions before using the result in a report or comparing it with another observation.

Input field	Meaning	Value type and bounds	Requirement
leftVolumesMm3	Left volumes mm3	list of number	Required
rightVolumesMm3	Right volumes mm3	list of number	Required
elapsedYears	Elapsed years	list of number	Required

Fit both regressions over increasing nonnegative years with positive left and right volumes.

bilateral = left+right; log change%/year = 100×slope(years,ln volume); linear loss = −slope(years,volume)

The symbols refer to the supplied quantities or generated values described above; unit conversions and comparison boundaries follow the stated implementation.

json

{
  "input": {
    "leftVolumesMm3": [
      3000,
      2700
    ],
    "rightVolumesMm3": [
      3000,
      2700
    ],
    "elapsedYears": [
      0,
      2
    ]
  },
  "output": {
    "bilateralVolumesMm3": [
      6000,
      5400
    ],
    "annualizedLogVolumeChangePercent": -5.26802578289125,
    "linearAnnualLossMm3": 300
  }
}

Limitations and assumptions

The log slope is not the exact compounded percentage 100(exp(slope)−1). Registration and segmentation consistency are not checked.

Numerical inputs must be finite. The tool checks its declared input shape and calculation constraints; those checks do not verify the quality, calibration or representativeness of the supplied observations.

Alternative or Complementary methods

Normalized Hippocampal Volume: Report bilateral hippocampal volume, ICV fraction and regression-based ICV adjustment using a supplied normative slope and reference; segmentation validity is external. Cognitive Decline Slope: Fit longitudinal cognitive score change after caller-supplied visit-specific practice offsets; score direction and diagnostic interpretation are not assumed.

Methodological background: Least squares estimation.^[1]

References

Least squares estimation — NIST/SEMATECH

Similar or alternative tools

Normalized Hippocampal Volume
Report bilateral hippocampal volume, ICV fraction and regression-based ICV adjustment using a supplied normative slope and reference; segmentation validity is external.
Cognitive Decline Slope
Fit longitudinal cognitive score change after caller-supplied visit-specific practice offsets; score direction and diagnostic interpretation are not assumed.