DOI: 10.3389/fnagi.2020.605317

One-Liner

An excercize scheme has had some measured effect on theta/alpha ratio and Brain wave frequency on AD patients; prognosis of AD not controlled for.

Novelty

• Leveraged physical training scheme and measured EEG effects by quantifying theta/alpha ratio

Notable Methods

• Used theta/alpha ratio as assay for improvement, and found the exercise scheme did so p<0.05
• Only tested patients with AD w/o a control for stage

Key Figs

Figure 1

This figure tells us th N number of participants through the study

DOI: 10.3389/fcomp.2021.624683

One-Liner

late fusion of multimodal signal on the CTP task using transformers, mobilnet, yamnet, and mockingjay

Novelty

• Similar to Martinc 2021 and Shah 2021 but actually used the the current Neural-Network state of the art
• Used late fusion again after the base model training
• Proposed that inconsistency in the diagnoses of MMSE scores could be a great contributing factor to multi-task learning performance hindrance

Notable Methods

• Proposed base model for transfer learning from text based on MobileNet (image), YAMNet (audio), Mockingjay (speech) and BERT (text)
• Data all sourced from recording/transcribing/recognizing CTP task

Key Figs

Figure 3 and 4

This figure tells us the late fusion architecture used

DOI: 10.3389/fnagi.2021.642033

One-Liner

Proposed cross-linguistic markers shared for AD patients between English and French; evaluated features found with standard ML.

Novelty

Multi-lingual, cross-linguistic analysis.

Notable Methods

• Looked at common patters between the two languages
• Linguistic results scored by IUs on CTP task

Key Figs

Figure 1

This figure tells us the various approaches measured.

Table 2

Here’s a list of semantic features extracted

Table 3

Here’s a list of NLP features extracted. Bolded items represent P <0.001 correlation for AD/NonAD difference between English and French.

DOI: 10.3389/fnagi.2020.607449

One-Liner

oral lexical retrieval works better than qualitative narrative analysis to classify dementia; and semantic fluency + Disfluency features chucked on an SVM returns pretty good results.

Novelty

Tried two different assays of measuring linguistic ability: oral lexical retrieval metrics, and qualitative discourse features analysis of speech.

Notable Methods

• Subjects divided into three groups

  • Great cog. decline
  • Impaired but stable
  • Healthy controls

• Administered BNT and SVF tests as baseline

  

Key Figs

Table 3

This figure tells us that the percentages of unrelated utterances was a statistically significant metric to figure differences between the three experimental groups.

DOI: 10.3389/fpsyg.2020.623237

One-Liner (thrice)

1. Used features extracted by VGGish from raw acoustic audio against a SVM, Perceptron, 1NN; got \(59.1\%\) classif. accuracy for dementia
2. Then, trained a CNN on raw wave-forms and got \(63.6\%\) accuracy
3. Then, they fine-tuned a VGGish on the raw wave-forms and didn’t report their results and just said “we discovered that audio transfer learning with a pretrained VGGish feature extractor performs better” Gah!

Novelty

Threw the kitchen sink to process only raw acoustic input, most of it missed; wanted 0 human involvement. It seems like last method is promising.