Home Featured Post Deep learning model built on neuroimaging data identifies “Brain Age Gaps” as markers of Alzheimer’s disease (AD)

Deep learning model built on neuroimaging data identifies “Brain Age Gaps” as markers of Alzheimer’s disease (AD)

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Brain Age Gap is a Com­pos­ite Bio­mark­er for Demen­tia Pathol­o­gy or Sever­i­ty (GEN):

Mayo Clin­ic sci­en­tists have devel­oped a com­pu­ta­tion­al mod­el that pre­dicts brain age using a large col­lec­tion of neu­roimag­ing data obtained using FDG-PET (flu­o­rodeoxyglu­cose positron emis­sion tomog­ra­phy) and struc­tur­al MRI (mag­net­ic res­o­nance imag­ing). The deep learn­ing-based mod­el tests the rela­tion­ship between brain age gaps in var­i­ous forms of demen­tia, includ­ing mild cog­ni­tive impair­ment (MCI), Alzheimer’s dis­ease (AD), fron­totem­po­ral demen­tia (FTD), and Lewy body demen­tia (LBD), as well as in nor­mal brains.

… “The abil­i­ty for deep learn­ing to accu­rate­ly pre­dict age based on brain imag­ing data has been known for some time. How­ev­er, look­ing at brain age gap or the dif­fer­ence between pre­dict­ed and actu­al age, has been thought to have the poten­tial to be uti­lized as a bio­mark­er. Oth­ers have argued that such a brain age gap is only able to mark treat­ment-lev­el bio­log­i­cal dif­fer­ences and is unable to track changes in state and there­fore should not be inter­pret­ed as accel­er­at­ed brain aging,” (Senior author of the study, Dr. David) Jones said. “The main find­ing of our study is that we could indeed find evi­dence that high brain age gap is behav­ing as an accel­er­at­ed brain aging biomarker.”

The Study:

Deep learn­ing-based brain age pre­dic­tion in nor­mal aging and demen­tia (Nature Aging).

Abstract: Brain aging is accom­pa­nied by pat­terns of func­tion­al and struc­tur­al change. Alzheimer’s dis­ease (AD), a rep­re­sen­ta­tive neu­rode­gen­er­a­tive dis­ease, has been linked to accel­er­at­ed brain aging. Here, we devel­oped a deep learn­ing-based brain age pre­dic­tion mod­el using a large col­lec­tion of flu­o­rodeoxyglu­cose positron emis­sion tomog­ra­phy and struc­tur­al mag­net­ic res­o­nance imag­ing and test­ed how the brain age gap relates to degen­er­a­tive syn­dromes includ­ing mild cog­ni­tive impair­ment, AD, fron­totem­po­ral demen­tia and Lewy body demen­tia. Occlu­sion analy­sis, per­formed to facil­i­tate the inter­pre­ta­tion of the mod­el, revealed that the mod­el learns an age- and modal­i­ty-spe­cif­ic pat­tern of brain aging. The ele­vat­ed brain age gap was high­ly cor­re­lat­ed with cog­ni­tive impair­ment and the AD bio­mark­er. The high­er gap also showed a lon­gi­tu­di­nal pre­dic­tive nature across clin­i­cal cat­e­gories, includ­ing cog­ni­tive­ly unim­paired indi­vid­u­als who con­vert­ed to a clin­i­cal stage. How­ev­er, regions gen­er­at­ing brain age gaps were dif­fer­ent for each diag­nos­tic group of which the AD con­tin­u­um showed sim­i­lar pat­terns to nor­mal aging.

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