Objective:

To review characteristic FDG-PET metabolic patterns associated with various antibodies
involved in autoimmune encephalitis and assess their role in diagnosis and disease
monitoring.

Background:

Autoimmune encephalitis encompasses a heterogeneous group of antibody-mediated
disorders affecting the central nervous system. While MRI findings may be normal or
nonspecific in early disease, ¹⁸ F-FDG-PET has emerged as a powerful tool in the evaluation
of autoimmune encephalitis, capable of detecting metabolic alterations before structural
changes occur. Recognizing characteristic metabolic patterns can help in the differentiation
of AE subtypes and in making immunotherapy decisions.

Design/Methods:

We conducted a literature search of 200 published studies, case series, and cohort analyses
evaluating brain FDG-PET findings in patients with autoimmune encephalitis associated with
antibodies against NMDAR, GABA_B receptor, LGI1, CASPR2, AMPAR, and other antigens.
Different patterns of hypermetabolism and hypometabolism were analyzed across subtypes
and correlated with disease stage and clinical presentation.

Results:

FDG-PET demonstrates high sensitivity in AE, often surpassing MRI in early detection.
Distinct metabolic signatures are noted among antibody subtypes:
In NMDAR encephalitis,frontotemporal and basal ganglia hypermetabolism with occipital
hypometabolism, occasionally reversing during recovery. In LGI1 encephalitis: Mesial
temporal and basal ganglia hypermetabolism, sometimes associated with faciobrachial
dystonic seizures. In GABA-B receptor encephalitis, marked limbic and temporal
hypermetabolism reflecting excitatory overactivity. In CASPR2 and AMPAR encephalitis,
variable limbic and cortical patterns, often mixed with hyper or hypometabolism. FDG-PET
abnormalities frequently normalize following immunotherapy, and correlate with clinical
improvement.

Conclusions:

FDG-PET shows characteristic, antibody-specific metabolic patterns in autoimmune
encephalitis, frequently preceding MRI changes. Recognition of these patterns enhances
early diagnosis, facilitates subtype differentiation, and provides a valuable biomarker for
treatment response and disease monitoring.