Table 1 Methods comparison.

From: A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease

   

AUC

Threshold

Specificity

Sensitivity

Accuracy

PPV

NPV

METHOD A (45 regions)

nADrp vs ADrp

T1w MRI

0.9047

−0.0387

0.8224

0.8362

0.8284

0.7823

0.8681

T1w MRI + scores

0.9971

−0.1969

0.9671

0.9310

0.9554

0.9558

0.9484

MCIAD vs AD

T1w MRI

0.7942

0.0648

1.0000

0.5185

0.7759

1.0000

0.7045

T1w MRI + scores

0.9656

0.8184

0.9384

0.8583

0.8633

0.9237

0.8839

METHOD B(45 + 70 regions)

nADrp vs ADrp

T1w MRI

0.9920

0.0938

0.9831

0.9741

0.9786

0.9826

0.9748

T1w MRI + scores

0.9859

0.6318

0.9830

0.9741

0.9786

0.9826

0.9747

MCIAD vs AD

T1w MRI

0.7984

0.2554

0.9516

0.5556

0.7672

0.9091

0.7108

T1w MRI + scores

0.9367

0.1428

0.8871

0.8333

0.8621

0.8654

0.8594

  1. The classification between nADrp and ADrp, as well as the classification between MCIAD and AD patients were tested with two methods.
  2. With Method A, the algorithm received as input features extracted from the 45 brain regions resulting from segmentation of the white matter (without and with the CSF/cognitive scores). Method B considered the features extracted from the 70 subcortical regions (without and with the CSF/cognitive scores).
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